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GPU Kernel Engineer
Sciforium
San Francisco, USA
Full-time
Senior · 5+ years experience
Hybrid
USD 190000-250000 yearly / year
Discovered 2 weeks ago
C++PTXCUDAROCmTritonJAX Pallas
Free
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C++PTXCUDA
Role Overview
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 Skills for This Role
C++PTXCUDAROCmTritonJAX Pallas
Full Job Posting
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.
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