CUDA Kernel Engineer

Requirements

• Design, implement, and optimize custom CUDA kernels for NVIDIA GPUs, with a focus on maximizing occupancy, memory throughput, and warp efficiency.
• Profile GPU workloads using tools such as Nsight Compute, Nsight Systems, nvprof, and CUDA‐MEMCHECK.
• Analyze and eliminate performance bottlenecks including warp divergence, uncoalesced memory access, register pressure, and PCIe transfer overhead.
• Improve GPU memory pipelines (global, shared, L2, texture memory) and ensure proper memory coalescing.
• Collaborate closely with AI systems, model acceleration, and backend distributed systems teams.
• Contribute to GPU architecture decisions, kernel libraries, and internal performance-engineering best practices.

Benefits

• Competitive salary & equity options
• Sign-on bonus
• Health, Dental, and Vision
• 401k