Member of Technical Staff, Kernel Engineering
At a glance
Senior Embedded & Firmware role at Inferact. San Francisco · full-time · $200,000–$400,000 base.
techroles summary, based on the employer's posting.
What you'll do
- Write GPU kernels and low-level optimizations to improve vLLM inference speed across accelerator hardware
- Work with hardware vendors as they bring new chips to vLLM, tuning performance for each generation
- Profile kernel performance and use optimization methods to find additional speedups
What you bring
- A bachelor's in computer science, engineering or a similar area, or equivalent practical experience
- Deep hands-on work writing CUDA kernels or comparable kernels with CuTeDSL, Triton, TileLang or Pallas
- Strong GPU architecture knowledge, plus high-performance C++ and Python skills
- Experience with profiling tools such as Nsight or rocprof and a track record of performance tuning
Who this fits
This senior performance engineering role suits someone focused on GPU kernels, accelerator architecture and benchmark-driven optimization. It is based onsite in San Francisco, with US remote work considered for exceptional candidates. Visa sponsorship is handled case by case.
From the employer
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:
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.
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.
Embedded & Firmware work from LinkedIn, X, Reddit and Upwork, in one list.
Each morning you get everything that fits, strongest first.
Embedded & Firmware$200k+
Find me workBy the team behind techroles.