Member of Technical Staff, Kernel Engineering

Inferact

$200K — $400K *
Consumer Technology
Less than 5 years of experience
Job Overview by Ladders

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.

Responsibilities

  • Write kernels and low-level optimizations for the vLLM inference engine.
  • Optimize code to achieve maximum performance across a wide array of hardware accelerators.
  • Collaborate directly with hardware vendors on performance extraction for new chip generations.
  • Analyze and optimize GPU architecture and memory usage for efficiency.
  • Utilize profiling tools to identify bottlenecks and implement performance improvements.

Benefits

  • Generous health, dental, and vision benefits.
  • 401(k) company match.
Full Job Description
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.

Similar Jobs

More Jobs at Inferact

More Consumer Technology Jobs

Find similar Member of Technical Staff, Kernel Engineering jobs: