GPU Kernel Engineer

Responsibilities

• Design, implement, and optimize custom GPU kernels using C++, PTX, CUDA, ROCm, Triton, and/or JAX Pallas.
• Profile and optimize ML operations for large-scale LLM training and inference.
• Integrate low-level GPU kernels into PyTorch, JAX, and custom internal runtimes.
• Develop performance models, identify bottlenecks, and deliver kernel-level performance improvements.
• Collaborate with ML researchers, distributed systems engineers, and model-serving teams.
• Work with NVIDIA and AMD hardware vendors on GPU architecture and compiler/toolchain capabilities.
• Contribute to tooling, documentation, benchmarking suites, and testing frameworks.

Requirements

• At least 5 years of industry or research experience in GPU kernel development or high-performance computing.
• Bachelor's, master's, or PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a related field.
• Strong programming skills in C++ and Python and familiarity with ML frameworks.
• Deep expertise in CUDA, ROCm, GPU memory models, and performance optimization.
• Hands-on experience with Triton and/or JAX Pallas.
• Strong understanding of PTX, GPU assembly, and low-level GPU execution.
• Extensive experience writing and optimizing custom GPU kernels in C++ and PTX.
• Experience integrating low-level kernels into PyTorch, JAX, or similar frameworks.
• Experience with large-scale LLM training or inference.
• Preferred experience includes AMD GPU and ROCm optimization, JAX FFI, custom ML operators, vLLM, TensorRT, TPUs, XLA, open-source ML systems, compilers, or GPU kernels.

Benefits

• Medical, dental, and vision insurance.
• 401(k) plan.
• Daily lunch, snacks, and beverages.
• Flexible time off.
• Competitive salary and equity.