GPU Kernel Engineer

Responsibilities

• Design, implement, and optimize custom GPU kernels using C++, PTX, CUDA, ROCm, Triton, and JAX Pallas.
• Profile ML operations, develop performance models, identify bottlenecks, and improve end-to-end performance for large-scale LLM training and inference.
• Integrate low-level GPU kernels into PyTorch, JAX, and custom internal runtimes.
• Collaborate with ML researchers, distributed systems engineers, model-serving teams, and NVIDIA/AMD hardware vendors.
• Contribute to tooling, documentation, benchmarking suites, and testing frameworks for correctness and performance reproducibility.

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 strategies.
• Hands-on experience with Triton and/or JAX Pallas for custom kernel development.
• Strong understanding of PTX, GPU ASM, 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 workloads for training or inference.
• Preferred qualifications include AMD GPU and ROCm optimization, JAX FFI, custom ML operator development, vLLM, TensorRT, TPUs, XLA, accelerator programming, and contributions to open-source ML systems, compilers, or GPU kernels.

Benefits

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