Member of Technical Staff, Kernel Engineering
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At a glance
Requirements
Credentials this posting asks for.
Job overview
Inferact is hiring a Member of Technical Staff, Kernel Engineering. Inferact seeks a performance engineer to develop high‑performance kernels and low‑level optimizations for vLLM, targeting a wide range of accelerators and collaborating with hardware vendors to maximize inference speed. The role involves writing CUDA and equivalent kernels, profiling, and benchmark‑driven performance tuning to keep vLLM the fastest inference engine.
Key focus areas include Write kernels and low‑level optimizations for vLLM across diverse accelerator types., Collaborate with hardware vendor teams to integrate new chips with vLLM., and Profile and benchmark code using tools such as Nsight and rocprof..
Successful candidates bring Bachelor's Degree In Computer Science, Engineering, Or Similar Or Equivalent Experience and Deep Experience Writing CUDA Kernels Or Equivalent. Important skills include CUDA Kernels, CuTeDSL, Triton, TileLang, Pallas, and GPU Architecture. Preferred (not required): ML-Specific Kernel Optimization, FlashAttention, Fused Kernels, and Quantization Techniques.
Skills & qualifications
Skills
Qualifications
Benefits
Full job description
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 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.
Skills and Qualifications Minimum qualifications:
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Bachelor's degree or equivalent experience in computer science, engineering, or similar.
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Deep experience writing CUDA kernels or equivalent (CuTeDSL, Triton, TileLang, Pallas).
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Strong understanding of GPU architecture: memory hierarchy, warp scheduling, tiling, tensor cores.
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Proficiency in C++ and Python with demonstrated ability to write high-performance code.
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Experience with profiling tools (Nsight, rocprof) and performance optimization methodologies.
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Obsession with benchmarks and squeezing every percentage point of speedup.
Preferred qualifications:
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Experience with ML-specific kernel optimization (FlashAttention, fused kernels).
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Knowledge of quantization techniques (INT8, FP8, mixed-precision).
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Familiarity with multiple accelerator platforms (NVIDIA, AMD, TPU, Intel).
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Experience with compiler technologies (LLVM, MLIR, XLA).
Bonus points if you have:
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Kernel-related contributions to vLLM or other inference engine projects.
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Contributions to open-source GPU, ML systems, or compiler optimization projects
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Written deep technical blogs on GPU optimization.
Logistics
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Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
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Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
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Visa sponsorship: We sponsor visas on a case-by-case basis.
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Benefits : Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
You've read the whole posting — now see how you match it.