Anyone AI is recruiting experienced GPU Kernel Engineers for a specialized project focused on reviewing, debugging, and evaluating high-performance compute kernels used in AI workloads. We’re looking for engineers with hands-on experience writing and optimizing kernels across frameworks such as CUDA, Triton, NKI, or Pallas, with a strong understanding of numerical correctness, GPU performance, memory optimization, and benchmarking. You’ll work with GPU and accelerator kernel tasks involving Kernel implementation and debugging CUDA and Triton optimization Translation between kernel frameworks Hardware migration Operator fusion Performance profiling and benchmarking Numerical correctness verification Compilation and runtime debugging Memory hierarchy optimization Kernel-level AI workload performance You’ll assess whether implementations are technically correct, efficiently designed, reproducible, and appropriately optimized for the target hardware. Requirements 3+ years of hands-on experience developing, optimizing, or debugging GPU or accelerator kernels Strong experience with at least two of the following: CUDA Triton NKI / AWS Neuron Pallas / JAX 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 Relevant Experience 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 Nice to have Experience across both NVIDIA GPU and custom accelerator ecosystems Experience with AWS Trainium, TPU, JAX, or other accelerators Compiler engineering experience 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 Engagement 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.
