GPU Kernel Engineer – CUDA, Triton & Accelerator Performance

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.

WHAT YOU’LL WORK ON

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.

WHAT WE’RE LOOKING FOR

- 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.