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.