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
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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
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• 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
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• 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
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• 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.

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