GPU Kernel Engineer

. Design, implement, and optimize custom GPU kernels using C++, PTX, CUDA, ROCm, Triton, and/or JAX Pallas .

Posted 9/26/2026full-timeSan Francisco • California • United StatesMid-LevelSenior💰 $190,000 - $250,000 per yearWebsite

Tools & technologies

AssemblyDistributed SystemsPythonPyTorchC++

Key responsibilities & impact

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, focusing on 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 improvements that accelerate AI workloads
Collaborate with ML researchers, distributed systems engineers, and model-serving teams to optimize compute performance across the stack
Work with NVIDIA and AMD hardware vendors and stay current on GPU architecture and compiler/toolchain improvements
Contribute to tooling, documentation, benchmarking suites, and testing frameworks for correctness and reproducibility
Build full-stack infrastructure powering frontier AI models and real-time applications at Sciforium, an AI infrastructure company

What you’ll need

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 with large-scale LLM workloads, training or inference
Experience with AMD GPUs and ROCm optimization
Familiarity with JAX FFI and custom ML operator development
Experience with efficient model serving frameworks such as vLLM or TensorRT
Experience with TPUs, XLA, or similar accelerator programming environments
Contributions to open-source ML systems, compilers, or GPU kernels
Legally authorized to work in the United States
Work visa sponsorship requirements must be disclosed

Comp & perks