GPU Kernel Engineer – CUDA, Triton & Accelerator Performance

Full Job Description

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.

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