GPU Kernel Engineer – CUDA, Triton & Accelerator Performance

Responsibilities

• 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.
• Comparing outputs against reference implementations
• Evaluating numerical tolerance thresholds
• Identifying performance bottlenecks and optimization opportunities

Requirements

• 3+ years of hands-on experience
• developing, optimizing, or debugging GPU or accelerator kernels
• Strong experience with at least
• two

Preferred

• 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

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

What You’ll Be Responsible For

Reviewing GPU and accelerator kernel implementations for correctness

• Reviewing kernel benchmarks and determining whether comparisons are fair
• 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

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.