Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.
We're looking for an infrastructure engineer to build the distributed systems that power inference at global scale. You'll design and implement the foundational layers that enable vLLM to serve models across thousands of accelerators with minimal latency and maximum reliability. Tomorrow, deploying a frontier model at scale should be as straightforward as spinning up a serverless database. The complexity doesn't disappear as it gets absorbed into the infrastructure you're building.
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, or similar.
Strong systems programming skills in Rust, Go, or C++.
Experience designing and building high-performance distributed systems at scale.
Understanding of network protocols and high-performance I/O.
Ability to debug complex distributed systems issues.
Preferred qualifications:
Experience with ML serving infrastructure and disaggregated inference architecture.
Familiarity with GPU programming models and memory hierarchies.
Knowledge of GPU interconnects (NVLink, InfiniBand, RoCE) and their performance characteristics.
Track record of improving system reliability and performance at scale.
Bonus points if you have:
Prior experience in supporting large‑scale model training or inference environments.
Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.
We're looking for a performance engineer to squeeze every FLOP out of modern accelerators. You'll write the kernels and low-level optimizations that make vLLM the fastest inference engine in the world. Your code will run on hundreds of accelerator types, from NVIDIA GPUs to emerging silicon. When hardware vendors develop new chips, they integrate with vLLM. You'll work directly with these teams to ensure we're extracting maximum performance from every generation of hardware.
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, or similar.
Deep experience writing CUDA kernels or equivalent (CuTeDSL, Triton, TileLang, Pallas).
Strong understanding of GPU architecture: memory hierarchy, warp scheduling, tiling, tensor cores.
Proficiency in C++ and Python with demonstrated ability to write high-performance code.
Experience with profiling tools (Nsight, rocprof) and performance optimization methodologies.
Obsession with benchmarks and squeezing every percentage point of speedup.
Preferred qualifications:
Experience with ML-specific kernel optimization (FlashAttention, fused kernels).
Knowledge of quantization techniques (INT8, FP8, mixed-precision).
Familiarity with multiple accelerator platforms (NVIDIA, AMD, TPU, Intel).
Experience with compiler technologies (LLVM, MLIR, XLA).
Bonus points if you have:
Kernel-related contributions to vLLM or other inference engine projects.
Contributions to open-source GPU, ML systems, or compiler optimization projects
Written deep technical blogs on GPU optimization.
Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.
We're looking for an cloud orchestration engineer to build the operational backbone that keeps vLLM running reliably at massive scale. You'll design the systems for cluster management, deployment automation, and production monitoring that enable teams worldwide to serve AI models without friction. You'll ensure that vLLM deployments are observable, debuggable, and recoverable, turning operational complexity into infrastructure that just works.
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, or similar.
Strong experience with Kubernetes and container orchestration at scale.
Experience designing and implementing custom Kubernetes operators.
Proficiency in Python/Rust/Go and infrastructure-as-code tools (Terraform, Helm, etc).
Experience managing GPU clusters and debugging hardware issues.
Ability to work across cloud platforms (AWS, GCP, Azure) and on-premise infrastructure.
Preferred qualifications:
Experience with ML-specific orchestration tools (Ray, Slurm).
Knowledge of GPU scheduling, multi-tenancy, and resource optimization.
Familiarity with vLLM deployment patterns and configuration.
Track record of improving operational reliability for ML systems.
Bonus points if you have:
Experience deploying inference systems on large-scale GPU (1,000+) clusters.
Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.
This is a globally remote opportunity. We're seeking exceptional generalist engineers who can work across the entire vLLM stack: from low-level GPU kernels to high-level distributed systems. This role is designed for self-directed, autonomous individuals who can identify the highest-leverage problems and solve them end-to-end without constant guidance.
You'll work asynchronously with our San Francisco headquarters while maintaining full ownership of critical infrastructure. You might be optimizing CUDA kernels one week, designing distributed orchestration systems the next, and implementing new model architectures the week after. The work you do will directly impact how the world runs AI inference.
Potential focus areas include:
Inference Runtime: Push the boundaries of LLM and diffusion model serving. Work at the core of vLLM to optimize how models execute across diverse hardware and architectures.
Kernel Engineering: Write the low-level kernels and optimizations that make vLLM the fastest inference engine in the world, running on hundreds of accelerator types.
Performance & Scale: Build the distributed systems that power inference at global scale—design foundational layers enabling vLLM to serve models across thousands of accelerators with minimal latency.
Cloud Orchestration: Build the operational backbone for cluster management, deployment automation, and production monitoring that enables teams worldwide to serve AI models without friction.
We're looking for engineers who thrive with autonomy. You should be able to take a vague problem statement and turn it into shipped code with minimal supervision. You communicate proactively, over-communicate context across time zones, and know when to ask for help versus when to push forward independently.
Core Requirements:
Bachelor's degree or equivalent experience in computer science, engineering, or similar
Demonstrated ability to work autonomously and drive projects to completion without close supervision
Excellent asynchronous communication skills and ability to collaborate effectively across time zones
Strong track record of shipping high-impact work in complex technical environments
Deep expertise in at least one of: systems programming, GPU/accelerator programming, distributed systems, or ML infrastructure
Technical Depth (strong in at least two):
CUDA kernels or equivalent (Triton, TileLang, Pallas) with deep understanding of GPU architecture
High-performance distributed systems in Rust, Go, or C++
Python with PyTorch internals and LLM inference systems (vLLM, TensorRT-LLM, SGLang)
Kubernetes, container orchestration, and infrastructure-as-code at scale
Transformer architectures, KV-cache memory management, and model serving
Preferred Qualifications:
Contributions to vLLM or other major open-source ML/systems projects
Experience with multiple accelerator platforms (NVIDIA, AMD, TPU, Intel)
Knowledge of quantization techniques, ML-specific kernel optimization, or compiler technologies
Track record of improving system reliability and performance at scale
Written widely-shared technical blogs or impactful side projects in the ML infrastructure space
Location: Fully remote, worldwide. We're timezone-flexible but expect regular overlap with Pacific Time for critical syncs.
Compensation: We offer competitive compensations (salary + equity) compared to the local market conditions.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers competitive benefits appropriate to your location, including health coverage where applicable.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We're looking for a TPU performance engineer to make vLLM a first-class inference engine on Google TPUs. You'll build and optimize TPU backends, compiler integrations, runtime paths, and benchmarking infrastructure using JAX, XLA, Pallas, and related tooling so vLLM can deliver frontier inference performance on TPU hardware.
You'll work at the boundary of inference systems, kernels, compilers, and hardware architecture, improving production-relevant model serving on TPU with clear correctness, latency, and throughput benchmarks. Your work will help make TPU support in vLLM usable, fast, benchmarked, and maintainable.
Skills and Qualifications
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, systems, machine learning, or similar.
Hands-on experience building or optimizing TPU workloads using JAX, XLA, Pallas, or related compiler and runtime tooling.
Deep understanding of TPU execution, memory behavior, compilation, and performance constraints for ML workloads.
Experience optimizing ML kernels or inference paths such as attention, GEMM, sampling, KV cache, fused kernels, or backend runtime paths.
Strong performance profiling and benchmarking skills, with the ability to use measurements, compiler artifacts, correctness tests, and reproducible benchmarks to guide optimization work.
Preferred qualifications:
Experience with vLLM, SGLang, TensorRT-LLM, XLA-based serving, or other LLM inference systems.
Familiarity with batching, KV cache, decoding, serving tradeoffs, and backend performance constraints in production inference systems.
Experience with compiler technologies such as XLA, MLIR, LLVM, Pallas, or other kernel DSLs, including lowering, fusion, and backend code generation.
Knowledge of quantization methods such as INT8, FP8, mixed precision, or TPU-specific numeric formats, including accuracy and performance tradeoffs.
Bonus points if you have:
Contributed to vLLM, JAX/XLA, Pallas, PyTorch/XLA, compiler projects, or other open-source ML infrastructure.
Built TPU benchmarking infrastructure or automated performance regression detection for accelerator workloads.
Worked directly with Google TPU ecosystem stakeholders, accelerator platform teams, or early-access programs to ship backend, compiler, or inference performance improvements.
Logistics
Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Overview
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We're looking for a Developer Relations Engineer to help make vLLM the default way developers understand, build, and scale AI inference. This is not a generic DevRel role. We're looking for a inference systems educator-builder: someone who can understand vLLM as a deep LLM inference systems project, teach hard technical concepts clearly, and create public artifacts that help practitioners build better systems.
You'll write technical deep dives, build demos, create tutorials, contribute to docs and examples, host workshops, and help developers understand topics like KV cache, continuous batching, prefix caching, prefill and decode, quantization, GPU serving, latency versus throughput, and model-server tradeoffs across vLLM and adjacent systems. Your work will shape how the broader AI infrastructure community learns, adopts, and builds with vLLM.
Skills and Qualifications
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, machine learning, systems, or similar.
Strong technical understanding of LLM inference systems, model serving, GPU inference, distributed runtimes, scheduling, batching, quantization, or related infrastructure.
Ability to credibly explain systems concepts such as KV cache, PagedAttention, continuous batching, prefill / decode scheduling, prefix caching, speculative decoding, tensor parallelism, data parallelism, or latency versus throughput tradeoffs.
Experience with vLLM or adjacent inference technologies such as SGLang, TensorRT-LLM, TGI, LoRAX, Ray Serve, FlashInfer, BentoML, Baseten-style serving platforms, or similar systems.
A strong public portfolio of technical artifacts, such as blogs, tutorials, workshops, courses, OSS docs, benchmark posts, architecture explainers, conference talks, demos, or runnable repositories.
Ability to write and teach for practitioners without sounding like a content marketer.
Strong engineering judgment, product taste, and ability to turn raw technical material into useful developer education.
Preferred qualifications:
Prior work in ML systems, distributed systems, HPC, compilers, GPU kernels, serving infrastructure, MLOps, developer tooling, or open-source infrastructure.
Experience creating technical content that teaches reusable mental models, not just product features.
Experience contributing to developer-facing open source through docs, tutorials, examples, cookbooks, demos, or community support.
Existing credibility or community presence in AI infrastructure, OSS, CUDA / GPU, Ray, vLLM, PyTorch, Modal, BentoML, Baseten, Predibase, Together AI, Anyscale, LMSYS, or similar ecosystems.
Ability to host workshops, create hands-on labs, present technical talks, and help developers move from concept to working code.
Bonus points if you have:
Written widely-shared technical blogs, courses, or architecture deep dives on LLM inference, model serving, GPU serving, or ML systems.
Built demos, benchmarks, tutorials, or repositories around vLLM, SGLang, TensorRT-LLM, TGI, Ray Serve, FlashInfer, or related systems.
Contributed to open-source ML infrastructure, inference systems, developer tooling, or technical education projects.
Created practitioner-facing content with code, diagrams, benchmarks, demos, or end-to-end labs.
Built a durable personal portfolio that demonstrates technical depth, taste, and a strong point of view.
Logistics
Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.
We're looking for an inference runtime engineer to push the boundaries of what's possible in LLM and diffusion model serving. Models grow larger. Architectures shift: mixture-of-experts, multimodal, agentic. Every breakthrough demands innovations on the inference engine itself. You'll work at the core of vLLM, optimizing how models execute across diverse hardware and architectures. Your work will directly impact how the world runs AI inference.
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, or similar.
Deep understanding of transformer architectures and their variants.
Strong programming skills in Python with experience in PyTorch internals.
Experience with LLM inference systems (vLLM, TensorRT-LLM, SGLang, TGI).
Ability to read and implement model architectures and inference techniques from research papers.
Demonstrate the ability to contribute performant and maintainable code and debug in complex ML codebases.
Preferred qualifications:
Deep understanding of KV-cache memory management, prefix caching, and hybrid model serving.
Familiarity with RL frameworks and algorithms for LLMs.
Experience with multimodal inference (audio/image/video/text).
Contributions to open-source ML or system infrastructure projects.
Bonus points if you have:
Implemented core features in vLLM or other inference engine projects.
Contributed to vLLM integrations (verl, OpenRLHF, Unsloth, LlamaFactory, etc).
Written widely-shared technical blogs or side projects on vLLM or LLM inference.
Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.
We're looking for an cloud orchestration engineer to build the operational backbone that keeps vLLM running reliably at massive scale. You'll design the systems for cluster management, deployment automation, and production monitoring that enable teams worldwide to serve AI models without friction. You'll ensure that vLLM deployments are observable, debuggable, and recoverable, turning operational complexity into infrastructure that just works.
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, or similar.
Strong experience with Kubernetes and container orchestration at scale.
Experience designing and implementing custom Kubernetes operators.
Proficiency in Python/Rust/Go and infrastructure-as-code tools (Terraform, Helm, etc).
Experience managing GPU clusters and debugging hardware issues.
Ability to work across cloud platforms (AWS, GCP, Azure) and on-premise infrastructure.
Preferred qualifications:
Experience with ML-specific orchestration tools (Ray, Slurm).
Knowledge of GPU scheduling, multi-tenancy, and resource optimization.
Familiarity with vLLM deployment patterns and configuration.
Track record of improving operational reliability for ML systems.
Bonus points if you have:
Experience deploying inference systems on large-scale GPU (1,000+) clusters.
Location: This role is based in Singapore.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is S$200,000 to S$400,000 annually + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers a generous benefits package, including medical, dental, and vision coverage.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.
We're looking for an inference runtime engineer to push the boundaries of what's possible in LLM and diffusion model serving. Models grow larger. Architectures shift: mixture-of-experts, multimodal, agentic. Every breakthrough demands innovations on the inference engine itself. You'll work at the core of vLLM, optimizing how models execute across diverse hardware and architectures. Your work will directly impact how the world runs AI inference.
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, or similar.
Deep understanding of transformer architectures and their variants.
Strong programming skills in Python with experience in PyTorch internals.
Experience with LLM inference systems (vLLM, TensorRT-LLM, SGLang, TGI).
Ability to read and implement model architectures and inference techniques from research papers.
Demonstrate the ability to contribute performant and maintainable code and debug in complex ML codebases.
Preferred qualifications:
Deep understanding of KV-cache memory management, prefix caching, and hybrid model serving.
Familiarity with RL frameworks and algorithms for LLMs.
Experience with multimodal inference (audio/image/video/text).
Contributions to open-source ML or system infrastructure projects.
Bonus points if you have:
Implemented core features in vLLM or other inference engine projects.
Contributed to vLLM integrations (verl, OpenRLHF, Unsloth, LlamaFactory, etc).
Written widely-shared technical blogs or side projects on vLLM or LLM inference.
Location: This role is based in Singapore.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is S$200,000 to S$400,000 annually + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers a generous benefits package, including medical, dental, and vision coverage.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.
We're looking for a performance engineer to squeeze every FLOP out of modern accelerators. You'll write the kernels and low-level optimizations that make vLLM the fastest inference engine in the world. Your code will run on hundreds of accelerator types, from NVIDIA GPUs to emerging silicon. When hardware vendors develop new chips, they integrate with vLLM. You'll work directly with these teams to ensure we're extracting maximum performance from every generation of hardware.
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, or similar.
Deep experience writing CUDA kernels or equivalent (CuTeDSL, Triton, TileLang, Pallas).
Strong understanding of GPU architecture: memory hierarchy, warp scheduling, tiling, tensor cores.
Proficiency in C++ and Python with demonstrated ability to write high-performance code.
Experience with profiling tools (Nsight, rocprof) and performance optimization methodologies.
Obsession with benchmarks and squeezing every percentage point of speedup.
Preferred qualifications:
Experience with ML-specific kernel optimization (FlashAttention, fused kernels).
Knowledge of quantization techniques (INT8, FP8, mixed-precision).
Familiarity with multiple accelerator platforms (NVIDIA, AMD, TPU, Intel).
Experience with compiler technologies (LLVM, MLIR, XLA).
Bonus points if you have:
Kernel-related contributions to vLLM or other inference engine projects.
Contributions to open-source GPU, ML systems, or compiler optimization projects
Written deep technical blogs on GPU optimization.
Location: This role is based in Singapore.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is S$200,000 to S$400,000 annually + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers a generous benefits package, including medical, dental, and vision coverage.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.
We're looking for an infrastructure engineer to build the distributed systems that power inference at global scale. You'll design and implement the foundational layers that enable vLLM to serve models across thousands of accelerators with minimal latency and maximum reliability. Tomorrow, deploying a frontier model at scale should be as straightforward as spinning up a serverless database. The complexity doesn't disappear as it gets absorbed into the infrastructure you're building.
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, or similar.
Strong systems programming skills in Rust, Go, or C++.
Experience designing and building high-performance distributed systems at scale.
Understanding of network protocols and high-performance I/O.
Ability to debug complex distributed systems issues.
Preferred qualifications:
Experience with ML serving infrastructure and disaggregated inference architecture.
Familiarity with GPU programming models and memory hierarchies.
Knowledge of GPU interconnects (NVLink, InfiniBand, RoCE) and their performance characteristics.
Track record of improving system reliability and performance at scale.
Bonus points if you have:
Prior experience in supporting large‑scale model training or inference environments.
Location: This role is based in Singapore.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is S$200,000 to S$400,000 annually + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers a generous benefits package, including medical, dental, and vision coverage.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We're looking for an AMD GPU performance engineer to make vLLM a first-class inference engine across the AMD accelerator ecosystem. You'll build and optimize AMD GPU backends, kernels, runtime paths, and benchmarking infrastructure using ROCm, HIP, Triton, CK, AITER, and related tooling so vLLM can deliver frontier inference performance on AMD GPUs.
You'll work at the boundary of inference systems, kernels, compilers, and hardware architecture, improving performance-critical paths such as attention, GEMM, sampling, KV cache, and communication-heavy operations. Your work will help make AMD GPU support in vLLM usable, fast, benchmarked, and maintainable.
Skills and Qualifications
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, systems, machine learning, or similar.
Hands-on experience optimizing AMD GPU workloads using ROCm, HIP, Triton, CK, AITER, or similar AMD ecosystem tools.
Deep understanding of AMD GPU execution, memory behavior, toolchains, kernel performance, and backend-specific performance constraints.
Experience optimizing ML kernels or inference paths such as attention, GEMM, sampling, KV cache, fused kernels, or communication-heavy runtime paths.
Strong performance profiling and benchmarking skills, with the ability to use measurements, hardware counters, correctness tests, and reproducible benchmarks to guide optimization work.
Preferred qualifications:
Experience with vLLM, SGLang, TensorRT-LLM, ROCm-based serving, or other LLM inference systems.
Familiarity with batching, KV cache, decoding, serving tradeoffs, and backend performance constraints in production inference systems.
Experience with compiler and kernel technologies such as Triton, MLIR, LLVM, CK, AITER, HIP, or other kernel DSLs and backend libraries.
Knowledge of quantization methods such as INT8, FP8, mixed precision, or AMD hardware-specific numeric formats, including accuracy and performance tradeoffs.
Bonus points if you have:
Contributed to vLLM, ROCm, HIP, Triton, CK, AITER, PyTorch, compiler projects, or other open-source ML infrastructure.
Built AMD GPU benchmarking infrastructure or automated performance regression detection for accelerator workloads.
Worked directly with AMD, accelerator platform teams, or early-access programs to ship backend, compiler, or inference performance improvements.
Logistics
Location: This role is based in Singapore.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is S$200,000 to S$400,000 annually + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers a generous benefits package, including medical, dental, and vision coverage.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We're looking for a TPU performance engineer to make vLLM a first-class inference engine on Google TPUs. You'll build and optimize TPU backends, compiler integrations, runtime paths, and benchmarking infrastructure using JAX, XLA, Pallas, and related tooling so vLLM can deliver frontier inference performance on TPU hardware.
You'll work at the boundary of inference systems, kernels, compilers, and hardware architecture, improving production-relevant model serving on TPU with clear correctness, latency, and throughput benchmarks. Your work will help make TPU support in vLLM usable, fast, benchmarked, and maintainable.
Skills and Qualifications
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, systems, machine learning, or similar.
Hands-on experience building or optimizing TPU workloads using JAX, XLA, Pallas, or related compiler and runtime tooling.
Deep understanding of TPU execution, memory behavior, compilation, and performance constraints for ML workloads.
Experience optimizing ML kernels or inference paths such as attention, GEMM, sampling, KV cache, fused kernels, or backend runtime paths.
Strong performance profiling and benchmarking skills, with the ability to use measurements, compiler artifacts, correctness tests, and reproducible benchmarks to guide optimization work.
Preferred qualifications:
Experience with vLLM, SGLang, TensorRT-LLM, XLA-based serving, or other LLM inference systems.
Familiarity with batching, KV cache, decoding, serving tradeoffs, and backend performance constraints in production inference systems.
Experience with compiler technologies such as XLA, MLIR, LLVM, Pallas, or other kernel DSLs, including lowering, fusion, and backend code generation.
Knowledge of quantization methods such as INT8, FP8, mixed precision, or TPU-specific numeric formats, including accuracy and performance tradeoffs.
Bonus points if you have:
Contributed to vLLM, JAX/XLA, Pallas, PyTorch/XLA, compiler projects, or other open-source ML infrastructure.
Built TPU benchmarking infrastructure or automated performance regression detection for accelerator workloads.
Worked directly with Google TPU ecosystem stakeholders, accelerator platform teams, or early-access programs to ship backend, compiler, or inference performance improvements.
Logistics
Location: This role is based in Singapore.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is S$200,000 to S$400,000 annually + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers a generous benefits package, including medical, dental, and vision coverage.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We're looking for an AMD GPU performance engineer to make vLLM a first-class inference engine across the AMD accelerator ecosystem. You'll build and optimize AMD GPU backends, kernels, runtime paths, and benchmarking infrastructure using ROCm, HIP, Triton, CK, AITER, and related tooling so vLLM can deliver frontier inference performance on AMD GPUs.
You'll work at the boundary of inference systems, kernels, compilers, and hardware architecture, improving performance-critical paths such as attention, GEMM, sampling, KV cache, and communication-heavy operations. Your work will help make AMD GPU support in vLLM usable, fast, benchmarked, and maintainable.
Skills and Qualifications
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, systems, machine learning, or similar.
Hands-on experience optimizing AMD GPU workloads using ROCm, HIP, Triton, CK, AITER, or similar AMD ecosystem tools.
Deep understanding of AMD GPU execution, memory behavior, toolchains, kernel performance, and backend-specific performance constraints.
Experience optimizing ML kernels or inference paths such as attention, GEMM, sampling, KV cache, fused kernels, or communication-heavy runtime paths.
Strong performance profiling and benchmarking skills, with the ability to use measurements, hardware counters, correctness tests, and reproducible benchmarks to guide optimization work.
Preferred qualifications:
Experience with vLLM, SGLang, TensorRT-LLM, ROCm-based serving, or other LLM inference systems.
Familiarity with batching, KV cache, decoding, serving tradeoffs, and backend performance constraints in production inference systems.
Experience with compiler and kernel technologies such as Triton, MLIR, LLVM, CK, AITER, HIP, or other kernel DSLs and backend libraries.
Knowledge of quantization methods such as INT8, FP8, mixed precision, or AMD hardware-specific numeric formats, including accuracy and performance tradeoffs.
Bonus points if you have:
Contributed to vLLM, ROCm, HIP, Triton, CK, AITER, PyTorch, compiler projects, or other open-source ML infrastructure.
Built AMD GPU benchmarking infrastructure or automated performance regression detection for accelerator workloads.
Worked directly with AMD, accelerator platform teams, or early-access programs to ship backend, compiler, or inference performance improvements.
Logistics
Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Overview
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We’re looking for a Product Marketing Manager to accelerate vLLM’s position as the leading open-source inference engine and publicize Inferact’s role in driving its development. This role sits at the intersection of product marketing, events, partner marketing, community, and growth. You’ll help turn a high volume of conference and ecosystem demand into a proactive GTM motion that builds brand awareness, community engagement, and qualified momentum around vLLM and Inferact
You’ll own end-to-end execution for conferences, partner events, hosted events, landing pages, speaker coordination, booth needs, collateral, follow-up workflows, and the many details that make technical events successful. You’ll develop and strengthen relationships between Inferact and major corporate and ecosystem partners, while shaping the messaging and presence of both vLLM and Inferact, while developing . You’ll work closely with product, engineering, leadership, partners, and design collaborators. This is a hands-on 0-to-1 role for someone with strong taste, high urgency, and an AI-native way of working.
Skills and Qualifications
Minimum qualifications:
Bachelor's degree or equivalent experience in marketing, product, computer science, business, communications, or a related field.
Strong product marketing or GTM fundamentals, including positioning, messaging, audience definition, narrative development, and practical execution.
AI-native operating style, with regular use of modern AI tools to accelerate writing, research, planning, landing pages, workflows, asset drafts, and execution.
Experience owning events, conferences, partner activations, launches, or community programs end-to-end, including logistics, timelines, vendors, speakers, assets, landing pages, and follow-up.
Strong taste and judgment around brand, tone of voice, event experience, marketing quality, and developer-facing credibility.
Ability to operate autonomously in a startup environment, proactively recommend what should happen next, and execute without needing every step prescribed.
Ability to work credibly with product, engineering, founders, partners, design agencies, and technical communities.
Preferred qualifications:
Experience marketing to developers, infrastructure engineers, ML engineers, technical founders, open-source communities, or AI / developer tools audiences.
Experience with AI infrastructure, developer tools, model serving, cloud platforms, open-source infrastructure, or other technical products.
Experience running partner marketing or co-marketing programs with ecosystem partners such as cloud providers, hardware companies, infrastructure platforms, or developer communities.
Ability to personally create or coordinate landing pages, event pages, simple websites, forms, briefs, and campaign assets using AI, no-code tools, or lightweight technical workflows.
Experience building 0-to-1 marketing programs, event playbooks, launch motions, or community programs in a startup or high-ambiguity environment.
Bonus points if you have:
Built an event or community strategy that materially improved brand awareness, partner momentum, developer mindshare, or qualified pipeline.
Worked in or around AI infrastructure, open-source infrastructure, developer tools, data infrastructure, cloud, GPU, or technical SaaS ecosystems.
Created high-quality technical marketing assets, launch narratives, event experiences, partner campaigns, or developer-facing content that you can walk through in detail.
Partnered with design agencies or external creative teams while maintaining speed, quality, and brand consistency.
Helped a startup move from reactive marketing execution to a proactive 6-12 month GTM, events, or community plan.
Logistics
Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Overview
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We're looking for a Site Reliability Engineer to make Inferact's vLLM-powered inference services dependable in production. You'll work alongside the engineers building the platform, bringing a reliability perspective to how systems are designed, shipped, and operated. This is a hands-on engineering role for someone who thinks about failure before launch, simplifies operations, and writes software that makes reliable inference possible at scale.
You'll help own reliability across the production lifecycle, from release safety and observability to incident response and recovery. You'll define SLOs around availability, latency, and successful inference requests; build monitoring that helps engineers pinpoint failures; and automate recurring operational work. When incidents occur, you'll drive mitigation, coordinate escalation, and lead post-mortems that result in concrete prevention work. You'll also partner with engineering on capacity planning and operational readiness so the platform can grow without becoming harder to operate.
Skills and Qualifications
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, systems, infrastructure, or similar.
Hands-on experience building or operating production distributed systems, cloud infrastructure, or services where reliability has meaningful user or business impact.
Strong programming or scripting ability in Python, Go, Bash, or similar, with experience building tools and automation that solve operational problems.
Experience responding to significant production incidents, including mitigation, root cause analysis, escalation, and following corrective actions through to completion.
Practical understanding of service-level objectives (SLOs), service-level indicators (SLIs), error budgets, and alerting that distinguishes user-impacting failures from noise.
Strong Linux, networking, and systems debugging fundamentals, with the ability to investigate failures across application and infrastructure boundaries.
Clear communication, sound judgment under pressure, and the ability to work with engineering teams to identify failure modes and make systems simpler to operate.
Preferred qualifications:
Experience supporting AI inference, model serving, ML infrastructure, GPU workloads, or other latency-sensitive, high-throughput services.
Experience deploying, operating, and debugging Kubernetes-based services, with familiarity with Docker, Terraform, or comparable infrastructure tooling.
Experience building observability with metrics, logs, traces, dashboards, and actionable alerts across distributed systems.
Experience improving deployment safety and recovery through automation, testing, release gates, and rollback procedures.
Experience with resource scheduling, capacity planning, or workload isolation in multi-tenant cloud or GPU environments.
Bonus points if you have:
Owned reliability for an inference service or other mission-critical platform through a period of significant growth.
Built automation that measurably reduced operational toil, improved recovery time, or prevented repeat incidents.
Established practical SLOs, post-mortem processes, or production-readiness reviews that helped engineering teams ship with greater confidence.
Logistics
Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: We offers generous health, dental, and vision benefits as well as 401(k) company match.
Overview
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We're looking for a Technical Product Marketing Manager to own product marketing for vLLM and Inferact across launches, positioning, technical narratives, partner announcements, and sales enablement.
This is a hands-on role for someone who can translate complex AI infrastructure into credible developer-facing messaging, work directly with engineers and ecosystem partners, and build repeatable launch systems that scale. You will own day-0 launch communications, product positioning, the benchmark narrative, customer proof, and the forward product marketing roadmap.
30-60-90 Day Milestones
30 Days:
Become the single owner for launch communications across vLLM and Inferact, including blog, social copy, partner coordination, and the internal readiness checklist.
Ship at least two launches as primary author and publish a launch playbook that engineers and partners can follow without additional direction.
Build a competitive teardown of recent launches and use it to establish the quality bar for future launches.
60 Days:
Own positioning and messaging, including personas, competitive talk tracks, and website product pages.
Own partnership announcements with ecosystem partners such as Google, AMD, and NVIDIA from messaging through launch day.
Own the sales collateral kit and standard vLLM plus Inferact talk template, and run enablement for engineers presenting externally.
Own product messaging across vLLM and Inferact, including technical product breakdowns, benefits, customer case studies, and supporting GTM materials in close partnership with GTM teams.
90 Days:
Present a 2027 product marketing plan covering launch tiers, the benchmark program, case study pipeline, and partner relationships.
Bring market and customer input into the product roadmap and pricing and packaging discussions.
Create a structured feedback loop between Marketing and Sales with Product and Engineering, ensuring customer, market, and GTM feedback consistently flows into product and launch decisions.
Build and operationalize a repeatable marketing process for launching vLLM and Inferact content, covering intake, messaging, review, distribution, partner amplification, and post-launch measurement.
Skills and Qualifications
Minimum qualifications:
Bachelor's degree in Computer Science, Mathematics, Engineering, or a related technical discipline, or equivalent experience as an engineer or developer.
Experience owning product marketing or developer relations for open-source software, LLM infrastructure, AI infrastructure, or developer tools.
Strong product marketing fundamentals across positioning, messaging, audience definition, narrative development, launches, and practical GTM execution.
Ability to write and edit technical launch content that is credible with developers, ML engineers, and infrastructure engineers.
Experience working cross-functionally with engineering, product, leadership, design, sales, and external partners.
Hands-on execution mindset with the ability to build launch systems, templates, and playbooks in a fast-moving startup environment.
Preferred qualifications:
Deep familiarity with AI infrastructure, model serving, vLLM, open-source infrastructure, cloud platforms, or GPU ecosystems.
Experience owning benchmark narratives, technical product pages, customer case studies, or developer-facing sales enablement.
Experience leading joint GTM with cloud providers, hardware vendors, model companies, or open-source ecosystem partners.
Track record building product marketing programs from 0 to 1 and turning reactive launch work into a proactive roadmap.
Bonus points if you have:
Direct experience marketing inference, model serving, or other performance-sensitive AI infrastructure.
Experience launching open-source projects or commercial products built around an open-source ecosystem.
Ability to create lightweight technical assets, landing pages, demos, or workflows using modern AI and no-code tools.
Logistics
Location: San Francisco, CA (On-site)
Compensation: Compensation determined based on background, skills, and experience. Offers include competitive base salary & meaningful equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.
About the Role
We're looking for a hands-on supercomputing infrastructure engineer to own and operate the high-performance compute infrastructure that keeps Inferact engineering productive. Inferact runs on expensive, high-performance GPU and HPC clusters across neo-cloud and dedicated compute providers, on heterogeneous hardware that spans NVIDIA GPUs, AMD GPUs, and Google TPUs. Your job is to make sure that infrastructure is healthy, available, observable, and usable around the clock.
You'll take ownership of cluster health, accelerator availability, monitoring, alerting, scheduling, access, diagnostics, and incident response across the systems our engineers rely on every day, from NVIDIA and AMD GPU clusters to TPU environments. You'll work closely with engineering leadership and infrastructure owners to standardize how we provision, operate, debug, and scale compute across providers and hardware platforms. Your work will directly impact how fast Inferact can build, test, and improve the systems powering vLLM.
Skills and Qualifications
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, systems administration, or similar.
Hands-on experience administering large compute clusters, HPC environments, university or research clusters, supercomputing systems, or production GPU clusters.
Strong Linux systems administration fundamentals across networking, processes, storage, package management, shell scripting, logs, access control, and system debugging.
Experience operating GPU servers, including driver management, GPU health monitoring, node failures, memory errors, scheduler issues, and hardware diagnostics.
Experience with cluster scheduling and resource allocation using SLURM, Kubernetes, or equivalent tooling.
Ability to own urgent infrastructure incidents end-to-end when compute issues are blocking engineering teams.
Ability to automate operational workflows using Bash, Python, Ansible, Terraform, Helm, or similar tooling.
Preferred qualifications:
Experience managing 1,000+ GPGPUs in production.
Experience operating GPU compute across providers such as Lambda, CoreWeave, Crusoe, Nebius, Together, Fireworks, RunPod, or similar environments.
Experience improving cluster utilization, reducing idle or unavailable GPU capacity, and debugging scheduling or resource contention issues.
Familiarity with high-performance GPU networking such as InfiniBand, RoCE, NVLink / NVSwitch, RDMA, NCCL, or equivalent systems.
Experience with storage for HPC or ML workloads, including NFS, Lustre, Ceph, distributed filesystems, or other high-throughput storage systems.
Experience managing secure access, identity, permissions, SSH, VPNs, bastion hosts, secrets, and basic infrastructure security hygiene.
Background in research computing, scientific computing, ML infrastructure, SRE, platform engineering, or infrastructure operations for engineering-heavy teams.
Bonus points if you have:
Managed GPU or HPC infrastructure in a university lab, national lab, research institution, AI infrastructure company, hedge fund, HFT firm, or large-scale ML platform team.
Built monitoring, alerting, runbooks, health checks, or remediation workflows that materially reduced operational toil or incident resolution time.
Operated Kubernetes clusters for ML or GPU workloads at meaningful scale.
Standardized provisioning, diagnostics, monitoring, and operating patterns across multiple compute providers.
Carried real operational responsibility for infrastructure used by many engineers or researchers.
Logistics
Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.
We're looking for an cloud orchestration engineer to build the operational backbone that keeps vLLM running reliably at massive scale. You'll design the systems for cluster management, deployment automation, and production monitoring that enable teams worldwide to serve AI models without friction. You'll ensure that vLLM deployments are observable, debuggable, and recoverable, turning operational complexity into infrastructure that just works.
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, or similar.
Strong experience with Kubernetes and container orchestration at scale.
Experience designing and implementing custom Kubernetes operators.
Proficiency in Python/Rust/Go and infrastructure-as-code tools (Terraform, Helm, etc).
Experience managing GPU clusters and debugging hardware issues.
Ability to work across cloud platforms (AWS, GCP, Azure) and on-premise infrastructure.
Preferred qualifications:
Experience with ML-specific orchestration tools (Ray, Slurm).
Knowledge of GPU scheduling, multi-tenancy, and resource optimization.
Familiarity with vLLM deployment patterns and configuration.
Track record of improving operational reliability for ML systems.
Bonus points if you have:
Experience deploying inference systems on large-scale GPU (1,000+) clusters.
Location: Fully remote, worldwide. We're timezone-flexible but expect regular overlap with Pacific Time for critical syncs.
Compensation: We offer competitive compensations (salary + equity) compared to the local market conditions.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers competitive benefits appropriate to your location, including health coverage where applicable.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
We're looking for a Forward Deployed Engineer to make Inferact successful inside real customer environments. You'll work directly with customers to deploy, integrate, debug, and optimize vLLM-powered inference systems across cloud, Kubernetes, GPU, networking, and model-serving environments.
This is a hands-on engineering role, not a traditional pre-sales position. You'll move from architecture discussions to implementation, own difficult production problems end-to-end, and work closely with core product and engineering teams to turn what you learn in the field into reusable product capabilities. Your work will directly affect customer time-to-value and how Inferact's platform evolves.
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, systems, machine learning, or similar.
Strong software engineering ability in Python, Go, TypeScript, or similar, with experience building production-quality integrations, tooling, services, automation, or prototypes.
Hands-on experience deploying or operating ML systems, model serving, AI infrastructure, cloud platforms, Kubernetes, or high-scale backend systems in production.
Ability to work directly with sophisticated customer engineering teams, understand ambiguous technical environments, and personally drive implementations and debugging to resolution.
Strong systems debugging skills across application, runtime, infrastructure, networking, identity, storage, observability, and distributed-system boundaries.
Ability to reason about latency, throughput, batching, model/runtime compatibility, scaling, reliability, and cost tradeoffs in production inference environments.
High ownership and strong technical communication, with the judgment to distinguish one-off customer work from problems that should become reusable product capabilities.
Preferred qualifications:
Experience with vLLM, SGLang, TensorRT-LLM, TGI, Ray Serve, BentoML, or other LLM inference and model-serving systems.
Experience with NVIDIA or AMD GPUs, CUDA / ROCm, GPU scheduling, multi-GPU serving, or accelerator-backed infrastructure.
Experience deploying infrastructure software into enterprise, regulated, security-sensitive, or bring-your-own-cloud environments.
Experience building APIs, SDKs, CLIs, developer tooling, deployment platforms, control planes, or infrastructure products used by technical teams.
Experience profiling latency, throughput, concurrency, GPU utilization, bottlenecks, and performance regressions.
Bonus points if you have:
Contributed to open-source ML systems, inference infrastructure, cloud infrastructure, Kubernetes, or developer tooling.
Worked in a forward-deployed, customer engineering, field engineering, or highly technical solutions role where you personally wrote and shipped code.
Built deployment playbooks, reference architectures, automation, or tooling that materially reduced customer time-to-production.
Resolved severe customer-facing production issues that crossed multiple technical layers and required close partnership with core engineering.
Turned repeated customer problems into reusable product features, abstractions, documentation, or platform improvements.
Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.
We're looking for an inference runtime engineer to push the boundaries of what's possible in LLM and diffusion model serving. Models grow larger. Architectures shift: mixture-of-experts, multimodal, agentic. Every breakthrough demands innovations on the inference engine itself. You'll work at the core of vLLM, optimizing how models execute across diverse hardware and architectures. Your work will directly impact how the world runs AI inference.
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, or similar.
Deep understanding of transformer architectures and their variants.
Strong programming skills in Python with experience in PyTorch internals.
Experience with LLM inference systems (vLLM, TensorRT-LLM, SGLang, TGI).
Ability to read and implement model architectures and inference techniques from research papers.
Demonstrate the ability to contribute performant and maintainable code and debug in complex ML codebases.
Preferred qualifications:
Deep understanding of KV-cache memory management, prefix caching, and hybrid model serving.
Familiarity with RL frameworks and algorithms for LLMs.
Experience with multimodal inference (audio/image/video/text).
Contributions to open-source ML or system infrastructure projects.
Bonus points if you have:
Implemented core features in vLLM or other inference engine projects.
Contributed to vLLM integrations (verl, OpenRLHF, Unsloth, LlamaFactory, etc).
Written widely-shared technical blogs or side projects on vLLM or LLM inference.
Location: Fully remote, worldwide. We're timezone-flexible but expect regular overlap with Pacific Time for critical syncs.
Compensation: We offer competitive compensations (salary + equity) compared to the local market conditions.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers competitive benefits appropriate to your location, including health coverage where applicable.
Overview
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We're looking for exceptional University of Waterloo co-op students who want to work on the systems that determine how fast, efficiently, and reliably frontier AI models run on frontier workloads at scale. This is not a sandboxed internship project. We will match your strengths and interests to a real engineering problem across the vLLM stack, from model execution and low-level accelerator code to distributed serving and the cloud platform that makes it all usable.
You'll work alongside the creators and core maintainers of vLLM on work intended to ship into open source, production systems, or the tooling that supports both. You will have a primary technical track, meaningful ownership, and mentorship from a small, senior team, with opportunities to collaborate across models, compilers, accelerators, networking, and distributed systems. The goal is to let exceptional students learn at the frontier while making contributions used by developers and AI teams around the world.
Potential Focus Areas
Your co-op will have a primary home in one of the following tracks, with opportunities to contribute across others:
Inference Runtime: Bring new model architectures and inference techniques to life in vLLM. Implement ideas from research papers; support mixture-of-experts, multimodal, diffusion, and agentic workloads; and improve scheduling, continuous batching, KV-cache memory management, prefix caching, and hybrid model serving.
Performance & Scale: Build the distributed serving data plane that lets vLLM run across many GPUs and nodes. Work on tensor, expert, or context parallelism; prefill/decode separation and KV-cache transport; fault tolerance and multi-tenancy; and high-performance communication using NCCL, DeepEP, NVSHMEM, RDMA, or InfiniBand.
Kernel Engineering: Raise the hardware performance ceiling by writing and optimizing attention, GEMM, sampling, KV-cache, fused, and quantization kernels. Use CUDA, Triton, TileLang, CUTLASS/CUTE, or related tools; reason about memory hierarchy, occupancy, and tensor cores; and prove speedups through profiling, correctness tests, and reproducible benchmarks.
AMD GPU Performance: Help make vLLM first-class on AMD accelerators. Work across ROCm, HIP, Triton, CK, AITER, kernels, runtime paths, quantization, compiler integration, and performance-regression infrastructure while learning how AMD-specific execution, memory, and toolchain constraints shape inference.
TPU Performance: Help make vLLM fast and correct on Google TPUs. Build backend, runtime, and compiler integrations with JAX, XLA, Pallas, MLIR, and related tooling; inspect compiler artifacts; work on lowering, fusion, and code generation; and benchmark production-relevant serving across correctness, latency, and throughput.
Cloud Orchestration: Build the operational platform that makes large-scale inference deployable and reliable. Work on Kubernetes and custom operators, topology-aware GPU scheduling, zero-downtime vLLM rollouts, token-aware routing, observability, infrastructure-as-code, automated recovery, and bring-your-own-cloud or multi-cloud fleet management.
You are not expected to arrive with experience in every track. We care most about unusual depth or learning velocity in one area, strong fundamentals, and evidence that you can turn a difficult problem into working, measurable software.
Skills and Qualifications
Minimum qualifications:
Currently pursuing a bachelor's, master's, or PhD degree in computer science, engineering, mathematics, or a related technical field, and eligible for a University of Waterloo co-op work term.
Strong programming ability in Python, C++, Rust, Go, or another systems-oriented language.
Strong computer science fundamentals and the ability to learn from research papers, technical documentation, and complex systems code.
Evidence that you have built and debugged nontrivial software through coursework, research, a prior internship, open-source work, or an ambitious side project.
A builder mindset: you define what success means, measure results, validate correctness, communicate clearly, and keep iterating until the system works.
Preferred qualifications:
Depth in at least one relevant area such as ML or inference systems, distributed systems, GPU or accelerator programming, compilers, high-performance computing, operating systems, networking, Kubernetes, or cloud infrastructure.
Hands-on experience with one or more relevant technologies such as PyTorch, vLLM, SGLang, TensorRT-LLM, CUDA, Triton, TileLang, ROCm/HIP, JAX/XLA, Pallas, Kubernetes, Helm, Terraform, Ray, or SLURM.
Experience profiling or benchmarking software, validating numerical or systems correctness, or building tests that protect against performance regressions.
Experience with systems beyond a single local process or device, including multi-GPU, multi-node, research-cluster, high-throughput, or production-like workloads.
Bonus points if you have:
Contributed to open-source ML, systems, compiler, or infrastructure projects, especially vLLM or adjacent projects.
Built a research system, benchmark suite, compiler or kernel project, distributed service, or infrastructure tool that demonstrates unusual technical depth and initiative.
Worked across more than one layer of the stack and enjoy moving between algorithms, runtimes, systems, and hardware rather than staying inside a single abstraction.
Created a technical artifact such as a paper, design document, blog post, demo, or talk that clearly explains what you built and what you learned.
Meet Inferact at Waterloo
Interested in learning more about Inferact, vLLM, and the engineering problems you could work on during your co-op? Join us for our University of Waterloo Employer Information Session to meet the team, learn more about Inferact, and hear about the Inference Engineering Co-op opportunity.
Venue: Tatham Centre, Room 2218
Date: Monday, October 5, 2026
Time: 5:00 PM – 7:00 PM ET
Logistics
Location: San Francisco, California. This co-op is in-office only at Inferact's San Francisco office and is intended for the University of Waterloo co-op program.
Work term: Co-op / internship. Exact dates will align with the applicable Waterloo work term.
Compensation: Competitive compensation based on the applicable co-op market and candidate background, plus a housing stipend for the duration of the co-op term.
Overview
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We are looking for an IT Systems & Operations Engineer to become the primary owner of the internal technology environment that keeps Inferact employees productive and secure. We have grown to north of 50 people since late January 2026 and plan to reach 75-80 employees by the end of 2026, with approximately 30% of the team based in APAC. As we scale, this person will own day-to-day IT support, employee onboarding and offboarding, application and account provisioning, endpoint management, identity and access, SaaS administration, office technology, and the ongoing cleanup and improvement of internal systems across our growing global team.
This is not a traditional help desk role, and it is not a CISO-level security position. We are looking for a hands-on operator who can own everything IT-related in our San Francisco office, including networking, meeting rooms, speakers, and other workplace technology, while also managing company-wide systems and security infrastructure. You will oversee remote device management and security patching across Mac, Windows, and Linux devices, administer VPN and access infrastructure including Cloudflare, Tailscale, and Axis, manage company software and tooling such as Notion, Okta, Vanta, Linear, and Vercel, and drive our SOC 2 compliance progress through Vanta in coordination with security firms and audit partners. The right person will combine strong service instincts with practical systems and security judgment, and will build automation and repeatable processes so Inferact can scale without unnecessary operational friction.
30-60-90 Day Milestones
30 Days: Take ownership of Inferact’s core IT operations, including employee support, San Francisco office technology, device management, application provisioning, onboarding, and access. Audit the current environment across networking, meeting rooms, employee devices, SaaS applications, VPNs, identity, permissions, endpoints, and security controls. Establish a clear view of the company’s current SOC 2 / Vanta status and open workstreams with security firms and audit partners.
60 Days: Standardize onboarding, offboarding, identity, access, endpoint security, remote device management, and patching workflows across Mac, Windows, and Linux devices. Clean up stale accounts, inconsistent permissions, unmanaged devices, and software ownership. Establish clear administration and operating standards for core tools including Notion, Okta, Vanta, Linear, Vercel, Cloudflare, Tailscale, and Axis, while beginning to automate repetitive IT processes and advancing priority SOC 2 controls.
90 Days: Build a scalable IT operations foundation with clear standards for office technology, devices, access, SaaS administration, VPN infrastructure, endpoint security, and employee security across the US and Singapore. Create repeatable monitoring, patching, compliance, and vendor-management processes, and establish a reliable operating cadence with security firms and audit partners so Inferact can continue scaling without unnecessary operational or security risk.
Skills and Qualifications
Minimum qualifications:
Hands-on experience owning application and systems provisioning, including onboarding new employees, managing ongoing access changes, and deprovisioning users cleanly when access is no longer needed.
Strong understanding of identity, access, endpoint security, VPN infrastructure, and remote device management, with hands-on experience managing and monitoring Mac, Windows, and Linux devices, security patches, and access controls.
Experience owning or materially supporting SOC 2 compliance programs, including coordinating evidence collection, remediation work, and communication with external security firms and audit partners.
Hands-on experience administering company-wide SaaS and productivity systems such as Okta, Notion, Linear, Vanta, Vercel, or similar tools, with strong judgment around permissions, lifecycle management, and system ownership.
Experience supporting office IT and workplace technology, including networking, meeting rooms, conferencing systems, speakers, and employee hardware, combined with excellent communication skills and the ability to operate independently across a fast-growing, highly technical organization.
Preferred qualifications:
Experience operating as a senior or early IT hire at a startup or rapidly growing technology company, ideally supporting a distributed or international team.
Prior experience supporting engineering-heavy or technically sophisticated organizations, including environments using modern identity, VPN, endpoint-management, cloud, and developer-tooling stacks.
Experience supporting employees and offices outside the US, including international hardware procurement and shipping (customs, duties, regional warranties), zero-touch device enrollment across regions, device retrieval from remote employees, and working with device-lifecycle or employer-of-record vendors to equip a distributed team.
Logistics
Location: San Francisco, CA (On-site).
Compensation: $125,000 – $170,000 base salary based on experience and background, plus meaningful startup equity.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
","descriptionPlain":"Overview\n\nInferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.\n\n\n\nAbout the Role\n\nWe are looking for an IT Systems & Operations Engineer to become the primary owner of the internal technology environment that keeps Inferact employees productive and secure. We have grown to north of 50 people since late January 2026 and plan to reach 75-80 employees by the end of 2026, with approximately 30% of the team based in APAC. As we scale, this person will own day-to-day IT support, employee onboarding and offboarding, application and account provisioning, endpoint management, identity and access, SaaS administration, office technology, and the ongoing cleanup and improvement of internal systems across our growing global team.\n\nThis is not a traditional help desk role, and it is not a CISO-level security position. We are looking for a hands-on operator who can own everything IT-related in our San Francisco office, including networking, meeting rooms, speakers, and other workplace technology, while also managing company-wide systems and security infrastructure. You will oversee remote device management and security patching across Mac, Windows, and Linux devices, administer VPN and access infrastructure including Cloudflare, Tailscale, and Axis, manage company software and tooling such as Notion, Okta, Vanta, Linear, and Vercel, and drive our SOC 2 compliance progress through Vanta in coordination with security firms and audit partners. The right person will combine strong service instincts with practical systems and security judgment, and will build automation and repeatable processes so Inferact can scale without unnecessary operational friction.\n\n\n\n30-60-90 Day Milestones\n\n - 30 Days: Take ownership of Inferact’s core IT operations, including employee support, San Francisco office technology, device management, application provisioning, onboarding, and access. Audit the current environment across networking, meeting rooms, employee devices, SaaS applications, VPNs, identity, permissions, endpoints, and security controls. Establish a clear view of the company’s current SOC 2 / Vanta status and open workstreams with security firms and audit partners.\n\n - 60 Days: Standardize onboarding, offboarding, identity, access, endpoint security, remote device management, and patching workflows across Mac, Windows, and Linux devices. Clean up stale accounts, inconsistent permissions, unmanaged devices, and software ownership. Establish clear administration and operating standards for core tools including Notion, Okta, Vanta, Linear, Vercel, Cloudflare, Tailscale, and Axis, while beginning to automate repetitive IT processes and advancing priority SOC 2 controls.\n\n - 90 Days: Build a scalable IT operations foundation with clear standards for office technology, devices, access, SaaS administration, VPN infrastructure, endpoint security, and employee security across the US and Singapore. Create repeatable monitoring, patching, compliance, and vendor-management processes, and establish a reliable operating cadence with security firms and audit partners so Inferact can continue scaling without unnecessary operational or security risk.\n \n \n\nSkills and Qualifications\n\nMinimum qualifications:\n\n - Hands-on experience owning application and systems provisioning, including onboarding new employees, managing ongoing access changes, and deprovisioning users cleanly when access is no longer needed.\n\n - Strong understanding of identity, access, endpoint security, VPN infrastructure, and remote device management, with hands-on experience managing and monitoring Mac, Windows, and Linux devices, security patches, and access controls.\n\n - Experience owning or materially supporting SOC 2 compliance programs, including coordinating evidence collection, remediation work, and communication with external security firms and audit partners.\n\n - Hands-on experience administering company-wide SaaS and productivity systems such as Okta, Notion, Linear, Vanta, Vercel, or similar tools, with strong judgment around permissions, lifecycle management, and system ownership.\n\n - Experience supporting office IT and workplace technology, including networking, meeting rooms, conferencing systems, speakers, and employee hardware, combined with excellent communication skills and the ability to operate independently across a fast-growing, highly technical organization.\n\nPreferred qualifications:\n\n - Experience operating as a senior or early IT hire at a startup or rapidly growing technology company, ideally supporting a distributed or international team.\n\n - Prior experience supporting engineering-heavy or technically sophisticated organizations, including environments using modern identity, VPN, endpoint-management, cloud, and developer-tooling stacks.\n\n - Experience supporting employees and offices outside the US, including international hardware procurement and shipping (customs, duties, regional warranties), zero-touch device enrollment across regions, device retrieval from remote employees, and working with device-lifecycle or employer-of-record vendors to equip a distributed team.\n\n\n\nLogistics\n\nLocation: San Francisco, CA (On-site).\n\nCompensation: $125,000 – $170,000 base salary based on experience and background, plus meaningful startup equity.\n\nBenefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.\n\n"},{"id":"98837c38-33a9-4085-97e7-1974dcf234a1","title":"Engineering Manager","department":"Research & Engineering","team":"Research & Engineering","employmentType":"FullTime","location":"San Francisco","secondaryLocations":[],"publishedAt":"2026-10-14T11:23:31.216+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"California","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/inferact/98837c38-33a9-4085-97e7-1974dcf234a1","applyUrl":"https://jobs.ashbyhq.com/inferact/98837c38-33a9-4085-97e7-1974dcf234a1/application","descriptionHtml":"Overview
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We're looking for an Engineering Manager to help scale a world-class, senior-heavy engineering organization working at the frontier of AI inference. You will lead a team of exceptional engineers across areas such as inference systems, performance, kernels, accelerators, and cloud infrastructure while creating the management capacity required for Inferact to keep growing quickly without sacrificing technical quality or execution speed.
This is a deeply technical management role. You will own team execution, hiring, coaching, prioritization, and operating rhythm, while staying close enough to the work to earn the trust of engineers solving hard systems problems. You will partner closely with engineering and company leadership to turn company priorities into clear technical direction, remove blockers, improve how the team works, and help every engineer do the highest-leverage work of their career.
Skills and Qualifications
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, or similar.
Demonstrated experience managing strong software engineers in technically demanding systems, infrastructure, ML systems, distributed systems, or performance-oriented environments.
Strong technical credibility with the ability to understand architecture, ask useful questions, challenge assumptions, and help unblock senior engineers without becoming a bottleneck or overriding domain experts.
Experience owning the full people-management lifecycle, including hiring, onboarding, 1:1s, feedback, performance management, career development, team health, and retention.
Ability to translate ambiguous company priorities into clear team goals, sequencing, ownership, and execution plans while maintaining urgency in a fast-changing startup environment.
Track record of building effective engineering operating rhythms around planning, decision-making, technical reviews, cross-team coordination, and accountability without creating unnecessary process.
Strong judgment about when to dive into a technical problem, when to delegate, and how to create leverage for senior engineers rather than competing with them for ownership.
2+ years of management experience with 10+ reports.
Preferred qualifications:
Experience leading teams working on AI / ML infrastructure, model serving, distributed systems, GPU infrastructure, compilers, performance engineering, cloud infrastructure, or developer platforms.
Experience managing senior, staff, principal, research-adjacent, or PhD engineers in an environment where technical credibility and autonomy are especially important.
Experience scaling an engineering team through rapid hiring, including interviewer calibration, hiring decisions, onboarding systems, and thoughtful team design.
Familiarity with inference systems and adjacent technologies such as vLLM, SGLang, PyTorch, CUDA / Triton, Kubernetes, distributed serving, or accelerator infrastructure.
Experience partnering closely with founders or engineering executives in an early-stage company where priorities, team boundaries, and organizational needs evolve quickly.
Bonus points if you have:
Built or scaled a small, unusually high-talent engineering team where the technical surface area was specialized and the quality bar was extremely high.
Previously remained hands-on as an engineer or technical lead before moving into management and can still reason credibly about complex production systems.
Managed teams spanning multiple technical specialties, geographies, or time zones while preserving strong ownership and communication.
Helped establish engineering management practices from an early stage rather than inheriting a mature management system.
Contributed to open-source infrastructure, AI systems, developer tools, or technically ambitious products used by external engineers.
Logistics
Location: This role is based in San Francisco, California and is in-office.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Overview
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We're looking for a Go-to-Market Engineer to build the commercial operating system for Inferact from the ground up. The GTM organization is early enough that core infrastructure such as CRM structure, account intelligence, standardized workflows, and repeatable operating processes are still being defined. You'll partner closely with commercial leadership to turn fragmented information into systems that make the team faster, more focused, and more effective.
This is primarily a hands-on GTM engineering role, combining technical building with a secondary RevOps remit. Early projects will include creating a clean commercial source of truth, helping define the sales process, mining Inferact's unique ecosystem and community data to identify high-value prospects and warm paths, and building the workflows and automation that support targeted selling. As the ICP becomes clearer, you'll help evolve the system toward scalable prospecting, reporting, and commercial execution without adding unnecessary process. As the sales team grows, you'll also help define rep onboarding and training and support sales compensation planning, with those broader RevOps needs expected from 2027 at the earliest.
Skills and Qualifications
Minimum qualifications:
Experience building or operating go-to-market systems, revenue operations, sales operations, or GTM engineering workflows in a startup or high-growth technology environment.
Hands-on experience implementing and structuring a CRM, including account and opportunity data models, pipeline stages, required fields, routing, process hygiene, and reporting.
Strong ability to work with data, APIs, integrations, enrichment sources, automation tools, and lightweight scripting to turn fragmented commercial information into usable workflows.
Understanding of how an early-stage sales organization operates, including ICP definition, prospect research, pipeline management, handoffs, follow-up, and the difference between useful process and unnecessary process.
Ability to take a blank-sheet problem, identify the highest-leverage operating system to build, and ship it without waiting for detailed requirements.
Strong communication and collaboration skills with the ability to work directly with commercial leadership, founders, product, engineering, and external implementation partners.
Ability to translate commercial needs into clear system and data requirements and communicate tradeoffs to both technical and non-technical stakeholders.
Preferred qualifications:
Experience building 0-to-1 GTM infrastructure before a company had a mature CRM, documented ICP, or standardized sales process.
Experience mining proprietary or community data sources to identify high-value accounts, warm paths, customer signals, or technically relevant prospects.
Experience with AI-native or modern GTM workflows, including research automation, enrichment, account intelligence, workflow orchestration, and highly personalized outbound support.
Experience supporting developer tools, AI infrastructure, open-source software, cloud platforms, or another highly technical B2B product.
Comfort combining hands-on GTM engineering with RevOps judgment; experience defining rep onboarding and training or supporting sales compensation plans is a plus for the role's longer-term scope.
Bonus points if you have:
Built a CRM and commercial operating cadence from scratch for an early-stage startup.
Created account research, enrichment, warm-intro mapping, or prospect prioritization systems that materially improved seller focus.
Automated repetitive GTM workflows while preserving high-quality human judgment for strategic accounts.
Worked with open-source communities or product-usage data as a source of commercial intelligence.
Built internal GTM tools, dashboards, agents, or data pipelines that became core infrastructure for a commercial team.
Logistics
Location: This role is based in San Francisco, California and is on-site.
Compensation: Compensation will be determined based on background, skills, and experience. Offers include equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Overview
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We're looking for a Social Media Manager to own the daily voice, publishing system, and audience growth strategy for vLLM and Inferact across developer-focused social channels.
You will manage brand accounts, founder communications, launch amplification, technical content, and community programming. The role requires strong technical judgment, fast execution, and the ability to make vLLM community content and Inferact company content feel distinct while operating as one coordinated launch system.
30-60-90 Day Milestones
30 Days: Outside Counsel Alignment & Contracting Intake
Take full control of daily posting, scheduling, and replies across the core vLLM and Inferact accounts.
Own the social side of the rebrand launch alongside the website.
Publish a voice guide that clearly separates the vLLM community voice from the Inferact company voice.
60 Days: Frontier AI & Regulatory Strategy
Run the founder program for the founders: Simon, Woosuk, Kaichao, and Roger with ghostwriting, an approval loop, and an agreed publishing cadence.
Launch and test channels such as Hacker News, Instagram, and TikTok using conference clips, event photos, and short technical explainers.
Own the partner amplification network so co-marketing posts are reshared by ecosystem partners on launch day.
Own a recurring video format such as a weekly vLLM feature explainer.
90 Days: Scalable Legal Infrastructure
Build a repeatable social launch system with tiered templates, asset checklists, pre-scheduled cross-posting, partner amplification, and post-launch reporting.
Set follower, engagement, and traffic targets for 2027 by account and channel, and report against them monthly.
Use 60 days of performance data to decide which channels to scale and which to stop.
Propose community programs such as contributor spotlights, creator programs, and employee advocacy, and define future team capacity needs.
Skills and Qualifications
Minimum qualifications:
Proven ownership and growth of developer-focused, open-source, or technical brand accounts across X, LinkedIn, and community channels such as Reddit or Hacker News.
Proven experience writing technical social content and ghostwriting for technology executives or founders.
Strong editorial judgment and ability to translate technical product, engineering, launch, and partner updates into credible social content.
Experience building content calendars, publishing workflows, approval systems, and launch amplification plans across multiple channels.
Ability to engage technical communities directly, respond quickly, and maintain distinct voices across community and company brands.
Comfort operating hands-on in a fast-moving startup environment with high publishing velocity.
Preferred qualifications:
Experience with AI, developer tools, cloud, infrastructure, open-source software, or technical SaaS audiences.
Experience running founder social programs, executive ghostwriting, or technical thought leadership programs.
Experience coordinating partner amplification with companies such as cloud providers, hardware vendors, open-source foundations, or developer platforms.
Experience creating short-form technical video, launch assets, performance reporting, and audience growth experiments.
Bonus points if you have:
Experience building a social function, creator program, contributor spotlight program, or employee advocacy program from 0 to 1.
Strong familiarity with developer communities and the norms of Hacker News, Reddit, X, and open-source ecosystems.
Ability to use modern AI tools to accelerate drafting, repurposing, research, scheduling, and performance analysis.
Logistics
Location: San Francisco, CA (On-site)
Compensation: Compensation determined based on background, skills, and experience. Offers include competitive base salary & meaningful equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Overview
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We're looking for a Graphic Designer to own the visual system for vLLM and Inferact across launches, technical content, partnerships, events, presentations, and web surfaces.
This is a hands-on design role for someone who can move quickly from technical concepts to polished diagrams, campaign assets, booth graphics, decks, and live Webflow pages. You will become the day-to-day owner of the brand system while building reusable design infrastructure for engineers and marketers.
30-60-90 Day Milestones
30 Days: Outside Counsel Alignment & Contracting Intake
Own every blog diagram, partnership announcement kit, social card, and preview image that ships, starting with vLLM blogs and upcoming partner launches.
Take over brand guidelines and Figma files and become the day-to-day contact for the agency.
Produce booth and print files for Inferact conference attendance and vLLM meetups.
60 Days: Frontier AI & Regulatory Strategy
Own the brand system across the website, decks, booths, merchandise, social, and docs with a design request intake and turnaround standard.
Build and publish website pages in Webflow, starting with event pages, partnership announcement pages, blog templates, and careers.
Build the vLLM technical diagram library in Figma that engineers can reuse in talks and blogs.
Own the talk slide template and sales deck design in partnership with the Technical Product Marketing Manager.
90 Days: Scalable Legal Infrastructure
Present a 2027 plan for the vLLM visual identity, motion and video style, conference brand, and Figma design system for marketing surfaces.
Own the website roadmap, including product pages, the public benchmark dashboard home, and conference sites for vLLM Conference US and China.
Set the direction for how the vLLM and Inferact visual identities relate and diverge.
Define future design capacity needs, including whether to add a second designer, motion designer, or expanded agency scope.
Skills and Qualifications
Minimum qualifications:
Strong working proficiency in Figma for brand systems, technical diagrams, reusable components, and marketing design.
Strong working proficiency in Webflow, with the ability to build, publish, and maintain live website pages without developer assistance.
Portfolio demonstrating complex technical diagrams, architecture flowcharts, and brand identities for developer, infrastructure, AI, or B2B technology companies.
Experience creating launch graphics, partner announcement assets, social cards, presentation templates, event graphics, booth files, and print-ready collateral.
Ability to translate technical concepts from engineers and product teams into clear, polished visual communication.
Strong systems mindset with the ability to create templates, libraries, guidelines, intake processes, and reusable design infrastructure.
Preferred qualifications:
Experience designing for AI, developer tools, cloud, infrastructure, open-source software, or other highly technical products.
Experience owning visual systems across web, decks, events, social, documentation, and merchandise.
Experience partnering with design agencies while maintaining internal speed, consistency, and quality.
Experience with motion, video, technical illustration, conference branding, or developer-facing data visualization.
Bonus points if you have:
Experience building a design system or brand function from 0 to 1 at a high-growth startup.
Experience creating reusable diagram libraries that engineers use in talks, blogs, and documentation.
Comfort using modern AI design tools to accelerate ideation, asset production, and iteration.
Logistics
Location: San Francisco, CA (On-site)
Compensation: Compensation determined based on background, skills, and experience. Offers include competitive base salary & meaningful equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.
About the Role
We're looking for a hands-on supercomputing infrastructure engineer to own and operate the high-performance compute infrastructure that keeps Inferact engineering productive. Inferact runs on expensive, high-performance GPU and HPC clusters across neo-cloud and dedicated compute providers, on heterogeneous hardware that spans NVIDIA GPUs, AMD GPUs, and Google TPUs. Your job is to make sure that infrastructure is healthy, available, observable, and usable around the clock.
You'll take ownership of cluster health, accelerator availability, monitoring, alerting, scheduling, access, diagnostics, and incident response across the systems our engineers rely on every day, from NVIDIA and AMD GPU clusters to TPU environments. You'll work closely with engineering leadership and infrastructure owners to standardize how we provision, operate, debug, and scale compute across providers and hardware platforms. Your work will directly impact how fast Inferact can build, test, and improve the systems powering vLLM.
Skills and Qualifications
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, systems administration, or similar.
Hands-on experience administering large compute clusters, HPC environments, university or research clusters, supercomputing systems, or production GPU clusters.
Strong Linux systems administration fundamentals across networking, processes, storage, package management, shell scripting, logs, access control, and system debugging.
Experience operating GPU servers, including driver management, GPU health monitoring, node failures, memory errors, scheduler issues, and hardware diagnostics.
Experience with cluster scheduling and resource allocation using SLURM, Kubernetes, or equivalent tooling.
Ability to own urgent infrastructure incidents end-to-end when compute issues are blocking engineering teams.
Ability to automate operational workflows using Bash, Python, Ansible, Terraform, Helm, or similar tooling.
Preferred qualifications:
Experience managing 1,000+ GPGPUs in production.
Experience operating GPU compute across providers such as Lambda, CoreWeave, Crusoe, Nebius, Together, Fireworks, RunPod, or similar environments.
Experience improving cluster utilization, reducing idle or unavailable GPU capacity, and debugging scheduling or resource contention issues.
Familiarity with high-performance GPU networking such as InfiniBand, RoCE, NVLink / NVSwitch, RDMA, NCCL, or equivalent systems.
Experience with storage for HPC or ML workloads, including NFS, Lustre, Ceph, distributed filesystems, or other high-throughput storage systems.
Experience managing secure access, identity, permissions, SSH, VPNs, bastion hosts, secrets, and basic infrastructure security hygiene.
Background in research computing, scientific computing, ML infrastructure, SRE, platform engineering, or infrastructure operations for engineering-heavy teams.
Bonus points if you have:
Managed GPU or HPC infrastructure in a university lab, national lab, research institution, AI infrastructure company, hedge fund, HFT firm, or large-scale ML platform team.
Built monitoring, alerting, runbooks, health checks, or remediation workflows that materially reduced operational toil or incident resolution time.
Operated Kubernetes clusters for ML or GPU workloads at meaningful scale.
Standardized provisioning, diagnostics, monitoring, and operating patterns across multiple compute providers.
Carried real operational responsibility for infrastructure used by many engineers or researchers.
Logistics
Location: This role is based in Singapore.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is S$200,000 to S$400,000 annually + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers a generous benefits package, including medical, dental, and vision coverage.
Overview
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We're looking for an Enterprise Partnerships Manager to own and grow Inferact's strategic relationships across the hardware and infrastructure ecosystem. Inferact works closely with major GPU and semiconductor partners, including organizations such as NVIDIA, AMD, and Intel, through commercial relationships that are strategically important to the company and require dedicated ownership beyond the technical work itself.
You'll serve as the commercial owner for a focused set of complex enterprise partnerships, coordinating across partner stakeholders and Inferact's engineering, product, legal, finance, and leadership teams. This role requires someone who understands open-source software, can navigate GPU and infrastructure ecosystems, and can operate comfortably between large enterprises and a fast-moving startup. The goal is to keep strategic partnerships moving, make the relationships easier to operate, and ensure they create leverage rather than drag for the core business. Existing partnerships are the immediate priority. Over time, you'll proactively reach out to new potential partners, including emerging accelerator companies, to build relationships and gather market intelligence even when no immediate formal partnership is planned.
Skills and Qualifications
Minimum qualifications:
Experience owning strategic partnerships, business development, enterprise accounts, or ecosystem relationships with large technology companies.
Strong commercial judgment with experience managing complex agreements, partner economics, multi-stakeholder negotiations, and ongoing relationship execution.
Experience working with GPU providers, hardware companies, cloud infrastructure partners, AI infrastructure vendors, or closely adjacent technical ecosystems.
Understanding of open-source software and the commercial dynamics that emerge when open-source technology sits at the center of a broader hardware and infrastructure ecosystem.
Ability to navigate complex enterprise organizations, identify the real decision-makers and dependencies, and maintain momentum across technical, legal, procurement, finance, and executive stakeholders.
Ability to operate with high autonomy in a startup where partnership structures, internal processes, and strategic priorities are still evolving. You should be comfortable researching prospects and initiating outreach yourself, without relying on an SDR.
Strong written and verbal communication with the ability to synthesize complex partner context into clear decisions, recommendations, and action plans.
Preferred qualifications:
Direct experience managing relationships with companies such as NVIDIA, AMD, Intel, hyperscalers, GPU clouds, semiconductor companies, or infrastructure providers.
Experience in AI infrastructure, model serving, developer infrastructure, open-source platforms, cloud computing, semiconductors, or enterprise technical partnerships.
Prior startup or high-growth company experience, especially where enterprise partners were materially larger and more complex than the company itself.
Experience coordinating commercial partnerships that also involve meaningful technical delivery, engineering dependencies, or joint product work.
Strong executive communication skills and the ability to represent a highly technical company credibly with senior external stakeholders.
Bonus points if you have:
Managed a major hardware or cloud partner from initial commercial agreement through delivery, expansion, and executive relationship management.
Worked in an open-source company where partners, customers, contributors, and potential competitors frequently overlapped.
Built operating cadences, account plans, and internal coordination mechanisms for a small set of strategically critical enterprise relationships.
Negotiated or managed high-value commercial agreements with technically complex deliverables and multiple internal stakeholders.
Successfully moved from a larger technology company into an early-stage startup and demonstrated comfort with ambiguity, speed, and broad ownership.
Logistics
Location: This role is based in San Francisco, California and is on-site.
Compensation: Compensation will be determined based on background, skills, and experience. Offers include equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
We're looking for a Member of Technical Staff, Vulnerability Management to own Inferact's side of vLLM's vulnerability-management process and help keep a critical piece of AI infrastructure secure and production-grade. You'll develop a deep understanding of vLLM's architecture, independently investigate vulnerability reports, and turn technical findings into clear, actionable work for the core team.
Working primarily in open source, you'll collaborate with vLLM maintainers, Red Hat counterparts, security firms, and researchers working with frontier AI models. You'll drive reports from initial triage through analysis and resolution, helping coordinate fixes, security advisories, and releases under the project's established process. You'll also identify practical security best practices that strengthen vLLM as it evolves. This is a hands-on product security role that combines technical investigation, sound judgment, and ownership of follow-through.
vLLM's vulnerability-management process
Minimum qualifications:
Hands-on product security experience, with a strong orientation toward infrastructure software and the security of complex software systems.
Ability to read source code, debug unfamiliar systems, reproduce reported issues, and explain root cause and practical security impact rather than simply forward findings.
Strong systems reasoning, including the ability to understand architecture, trust boundaries, deployment assumptions, and how different software components interact.
Ability to learn vLLM's core architecture and independently manage vulnerability investigations while bringing in maintainers where their expertise is needed.
Sound prioritization and risk-assessment judgment, with clear documentation of evidence, affected behavior, remediation needs, and next steps.
Strong written and verbal communication, discretion with sensitive reports, and the ability to work constructively with engineers, researchers, and external security collaborators.
Preferred qualifications:
Experience with vulnerability management and security best practices for open-source infrastructure software.
Experience coordinating vulnerability resolution across reporters, maintainers, security teams, and software releases.
Familiarity with vLLM, inference engines, ML infrastructure, or similarly complex distributed software; prior vLLM contributions are helpful but not required.
Experience preparing security advisories, assessing affected versions, and working with CVE and coordinated-disclosure workflows.
Experience translating security findings into practical architecture reviews, regression tests, secure development practices, or deployment guidance.
Bonus points if you have:
Helped resolve vulnerabilities in an open-source infrastructure project and can explain your technical contribution and coordination with maintainers.
Built security tooling or automation that improved investigation quality or reduced repetitive triage work.
Turned recurring vulnerability patterns into maintainable fixes, tests, or documentation that helped prevent similar issues.
Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Applicable benefits will be confirmed based on the final role location.
Overview
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We're looking for a Corporate Controller to build and lead the accounting and financial operations that support Inferact's growth. You'll establish dependable books, a disciplined close, and reporting that leadership can trust, while translating customer agreements, infrastructure spending, and international operations into clear accounting policies and reliable workflows. This is a hands-on role for someone who can resolve a difficult accounting issue themselves and build the processes, systems, and team capacity around it.
You'll oversee financial reporting, technical accounting, audit coordination, and the transaction processes behind payments, payroll, equity records, billing, collections, and cash controls. Working with engineering, GTM, Legal, People, external advisors, and the Strategic Finance Lead, you'll connect contracts and product usage to accurate invoices and revenue records, strengthen controls as complexity increases, and automate repetitive work without sacrificing review. The Strategic Finance Lead will lead planning and forecasting; you'll provide the accounting foundation and financial operations that make those plans credible.
Skills and Qualifications
Minimum qualifications:
Progressive accounting experience with meaningful ownership as a Controller, Assistant Controller, Head of Accounting, or equivalent leader in a growing company.
Strong US GAAP knowledge and practical technical-accounting judgment, including revenue recognition, stock-based compensation, accruals, and the accounting implications of commercial agreements.
Personal ownership of a complete close and financial statements, including reconciliations, journal-entry review, consolidation, and resolution of material accounting issues.
Hands-on experience across financial operations, including payables, receivables, billing, collections, payroll coordination, and cash-management controls.
Experience coordinating external audits and building documented policies, supporting schedules, review procedures, and effective internal controls.
Experience implementing or improving an accounting system and related workflows, with a track record of making financial data more reliable and reducing manual work.
Clear communication, sound judgment, and the ability to work independently with operational leaders, external accountants, legal advisors, and a small internal team.
Preferred qualifications:
CPA or equivalent professional accounting qualification; public-accounting experience combined with hands-on operating experience is valuable.
Experience in AI infrastructure, cloud, developer tools, software, or another technology business with complex commercial terms or consumption-based billing.
Experience with international entities, intercompany accounting, foreign currency, and coordination with local accounting, payroll, or tax advisors.
Experience evaluating leases, fixed assets, infrastructure commitments, and other material arrangements relevant to a compute-intensive business.
Experience working with FP&A on management reporting, cost classification, and cash visibility while maintaining a clear distinction between accounting conclusions and forecast assumptions.
Bonus points if you have:
Built a company's accounting function through a period of rapid growth rather than only administering an established process.
Connected contract terms and usage data to accurate invoicing, revenue accounting, and collections in a technical product business.
Led an ERP migration or financial-system integration without disrupting reporting, controls, or day-to-day operations.
Improved close quality, audit preparedness, or financial operations through automation while maintaining a clear review trail.
Logistics
Location: This role is based in San Francisco, California and is on-site.
Compensation: The compensation package, including base salary and equity terms, is to be determined based on the final role scope, background, skills, and experience.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Overview
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We're looking for a Strategic Finance Lead to help shape how Inferact grows, invests, and scales. You'll turn questions about inference economics, pricing, commercial opportunities, compute spending, and hiring into rigorous financial models and clear recommendations. This is a hands-on role for someone who can move from an ambiguous business question to the underlying data, build the analysis themselves, and help leadership make a better decision.
You'll partner closely with founders, engineering, infrastructure, go-to-market, People, and the Corporate Controller to evaluate growth and investment priorities, understand customer and product profitability, and make resource tradeoffs explicit. Alongside this strategic work, you'll own the operating model, budgeting, rolling forecasts, headcount and expense planning, and cash runway scenarios. You'll also support board discussions and fundraising with clear financial narratives and decision materials. The Controller will own accounting and reliable actuals; you'll translate those inputs into a forward-looking view of the business and practical recommendations.
Skills and Qualifications
Minimum qualifications:
Experience personally owning strategic finance, business planning, or financial analysis for a growing technology business, with evidence that your work influenced operating or investment decisions.
Strong financial modeling skills, including driver-based revenue and expense forecasts, headcount plans, and the ability to connect the income statement, balance sheet, and cash flow.
Commercial and operating judgment: you can evaluate unit economics, investment returns, pricing, and resource allocation while making assumptions and uncertainty explicit.
Ability to reconcile plans with accounting actuals, explain performance variances, and distinguish changes in business performance from timing, data, or modeling issues.
Advanced spreadsheet and analytical skills, with the ability to combine financial and operational data into models that others can inspect, maintain, and use.
Strong communication and business-partnering skills, including the ability to challenge assumptions constructively and explain financial tradeoffs to technical and non-financial leaders.
Comfort owning ambiguous finance projects end-to-end, balancing strategic analysis with recurring planning and forecasting, and prioritizing useful decisions over unnecessary process.
Preferred qualifications:
Experience with AI infrastructure, cloud platforms, developer tools, usage-based software, or businesses where infrastructure cost and customer consumption materially affect margins.
Experience partnering with engineering and infrastructure teams on compute costs, capacity commitments, utilization, and the financial implications of different deployment models.
Experience preparing board reporting, fundraising models, investor analyses, or decision materials for senior leadership.
Experience planning for a distributed or international organization, including headcount, hiring timing, and geographic cost differences.
Experience improving planning systems, dashboards, data integrations, or automated reporting; SQL or lightweight scripting is helpful but not required.
Bonus points if you have:
Built strategic finance models, decision processes, or a planning foundation before a company had an established finance operating cadence.
Connected product usage, infrastructure costs, and commercial terms to customer or product profitability and influenced a concrete business decision.
Improved forecast quality and management visibility without slowing execution or requiring a large finance team.
Built trusted, self-service financial reporting or used automation to reduce repetitive analysis while preserving review and data quality.
Logistics
Location: This role is based in San Francisco, California and is on-site.
Compensation: The compensation package, including base salary and equity terms, is to be determined based on the final role scope, background, skills, and experience.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.