5+ years in GPU kernel development or high-performance computing
Bachelor's, Master's, or PhD in relevant fields
Strong programming skills in C++ and Python
Deep expertise in CUDA/ROCm and performance optimization
Hands-on experience with Triton and/or JAX Pallas
Strong understanding of PTX and low-level GPU execution
Proven ability to integrate kernels into ML frameworks
Responsibilities
Design and optimize custom GPU kernels using C++, PTX, CUDA, and more
Profile and enhance performance of ML operations for large-scale training
Integrate GPU kernels into frameworks like PyTorch and JAX
Develop performance models and identify bottlenecks
Collaborate with ML researchers and systems engineers for performance optimization
Engage with hardware vendors to stay updated on GPU advancements
Contribute to documentation and testing frameworks for performance reproducibility
Benefits
Medical, dental, and vision insurance
401k plan
Daily lunch, snacks, and beverages
Flexible time off
Competitive salary and equity
Full Job Description
About the role
We are seeking a highly skilled GPU Kernel Engineer who is passionate about pushing the limits of performance on modern accelerators. In this role, you will design and optimize custom GPU kernels that power next-generation large-scale AI systems. You will work across the hardware-software stack, from low-level kernel development to integrating optimized ops into high-level ML frameworks used for large-scale training and inference.
This role is ideal for someone who thrives at the intersection of GPU programming, systems engineering, and cutting-edge AI workloads, and who wants to make meaningful contributions to the efficiency and scalability of our ML platform.
Key Responsibilities
Design, implement, and optimize custom GPU kernels using C++, PTX, CUDA, ROCm, Triton, and/or JAX Pallas.
Profile and optimize end-to-end performance of ML operations, with a focus on large-scale LLM training and inference.
Integrate low-level GPU kernels into frameworks such as PyTorch, JAX, and custom internal runtimes.
Develop performance models, identify bottlenecks, and deliver kernel-level improvements that significantly accelerate AI workloads.
Collaborate with ML researchers, distributed systems engineers, and model-serving teams to optimize compute performance across the stack.
Work closely with hardware vendors (NVIDIA/AMD) and stay current on the latest GPU architecture capabilities and compiler/toolchain improvements.
Contribute to tooling, documentation, benchmarking suites, and testing frameworks to ensure correctness and performance reproducibility.
Must-Haves
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++, 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.
Proven ability to integrate low-level kernels into PyTorch, JAX, or similar frameworks.
Experience working with large-scale LLM workloads (training or inference).
Nice-to-Haves
Experience with AMD GPUs and ROCm optimization.
Familiarity with JAX FFI and custom ML operator development.
Experience with efficient model serving frameworks (e.g., vLLM, TensorRT).
Experience with TPUs, XLA, or similar accelerator programming environments.
Contributions to open-source ML systems, compilers, or GPU kernels.
