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.

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