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