GPU Kernel Engineer – CUDA, Triton & Accelerator Performance
About The Position
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
Nice To Haves
- 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
Responsibilities
- 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
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Part-time
Career Level
Senior
Education Level
No Education Listed
