Senior GPU Compute & AI Kernel Engineer

Responsibilities

• Design, implement and optimize high-performance compute kernels for GPUs and AI accelerators using modern C++ and one or more of CUDA, SYCL, OpenCL, Triton or Apple Metal
• Profile, analyze and tune parallel algorithms for performance and memory efficiency, identifying and removing bottlenecks across compute, bandwidth and occupancy
• Accelerate machine learning workloads in PyTorch or TensorFlow, including the implementation of custom operators and kernels for Transformer, LLM and CNN architectures
• Map deep learning operators onto target accelerator architectures, taking advantage of memory hierarchies, tensor units and vendor-specific execution models
• Collaborate with compiler engineers on kernel code generation and lowering paths, working with technologies such as LLVM and MLIR
• Partner with hardware and architecture teams to evaluate performance characteristics of new accelerators and inform hardware/software co-design decisions
• Establish and maintain performance benchmarking, regression tracking and profiling methodology for kernel and workload performance
• Debug complex correctness and performance issues spanning framework, runtime, compiler and hardware layers
• Contribute to accelerator software stacks, runtimes and framework integrations, upstream where applicable
• Apply and uphold software engineering best practices across Linux development environments — version control, code review, automated testing and CI/CD

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