anyone-ai - GPU Kernel Engineer – CUDA, Triton & Accelerator Performance

• 3+ years of hands-on experience developing, optimizing, or debugging GPU or accelerator kernels • Strong experience with at least two of the following: • NKI / AWS Neuron • Strong understanding of GPU performance optimization • Experience with kernel profiling tools such as Nsight, NCU, roofline analysis, or framework-native profilers • Understanding of: • Memory bandwidth • Compute throughput • GPU occupancy • Shared memory • Register pressure • Memory coalescing • Bank conflicts • Strong understanding of floating-point numerical correctness and tolerance thresholds • Experience debugging kernel compilation and runtime issues • Ability to distinguish software defects, environment problems, and genuine optimization challenges • Candidates should have experience with several of the following types of work: • Writing kernels from technical specifications • Translating kernels between CUDA, Triton, or other frameworks • Migrating kernels across hardware platforms • Debugging incorrect kernel implementations • Optimizing kernel performance • Fusing multiple operations into optimized kernels • Experience across both NVIDIA GPU and custom accelerator ecosystems • Experience with AWS Trainium, TPU, JAX, or other accelerators • Familiarity with MLIR, XLA, or intermediate representation lowering • Contributions to GPU or ML kernel libraries • Experience with cuBLAS, cuDNN, Triton community kernels, or JAX/XLA custom calls • Experience with AI model evaluation, RLHF, or technical benchmark development • WHAT YOU’LL BE RESPONSIBLE FOR • Reviewing GPU and accelerator kernel implementations for correctness • Comparing outputs against reference implementations • Evaluating numerical tolerance thresholds • Reviewing kernel benchmarks and determining whether comparisons are fair • Identifying performance bottlenecks and optimization opportunities • Assessing whether performance targets are realistic given hardware limits • Reviewing kernel translations and hardware migrations • Identifying compilation, driver, memory, shape, and runtime issues • Determining whether technical tasks are genuinely difficult or incorrectly configured • Providing clear, actionable technical feedback • Work Type: Remote • Engagement: Part-time, project-based consulting • Focus: GPU kernels, performance engineering, debugging, and technical evaluation • This role is ideal for engineers who enjoy working close to the hardware, optimizing GPU workloads, debugging low-level performance issues, and pushing AI compute systems toward their performance limits.