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.

Proven track record building NVIDIA CUDA kernels from scratchnot just calling existing libraries.
Strong ability to optimize kernels (tiling strategies, occupancy tuning, shared memory design, warp scheduling).
Deep understanding of CUDA threads, warps, blocks, and grids, GPU memory hierarchy and memory coalescing, as well as warp divergence (how to detect, analyze, and mitigate it)
Experience diagnosing PCIe bottlenecks and optimizing host-device transfers (pinned memory, streams, batching, overlap).
Familiarity with C++, CUDA runtime APIs, and GPU debugging/profiling tooling.

CUDA Kernel Engineer

Key skills