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.

Source: EU Remote Jobs (https://euremotejobs.com/job/gpu-kernel-engineer-cuda-triton-accelerator-performance/)