This job post has expired on September 26, 2026. It is likely that the position has already been filled.
GPU Kernel Expert at Mercor
posted 1 month agoGPU Kernel Expert | $70–90/hr | Remote (US)
Join a cutting-edge AI evaluation project where your deep expertise in GPU and accelerator kernel development will directly shape the quality of frontier AI model training. You'll assess and provide rubric-based feedback on kernel development tasks — evaluating correctness, performance, and validity across a wide range of kernel types.
What You'll Do
- Evaluate the quality, correctness, and completeness of GPU/accelerator kernel development tasks used to train and assess frontier AI models
- Assess numerical correctness, performance-benchmarking fairness, task scoping, and compilation/runtime validity
- Provide clear, structured written feedback based on defined rubrics across diverse kernel task types
Basic Qualifications
- 3+ years of hands-on experience developing, optimizing, or verifying GPU/accelerator kernels in at least two of: CUDA, Triton, NKI, or Pallas (JAX)
- Strong understanding of numerical-correctness criteria (absolute/relative/ULP tolerances, reference-implementation selection)
- Demonstrated experience with performance profiling and benchmarking tools such as Nsight, ncu, roofline analysis, or framework-native profilers
- Familiarity with common compilation and runtime failure modes (driver mismatches, OOM, launch-configuration errors, shape/stride mismatches, autotuning failures)
- Experience with at least three kernel task types: generation from specification, translation/lowering across frameworks, hardware migration, debugging, performance optimization, or operator fusion
Preferred Qualifications
- Experience across both NVIDIA GPU (CUDA/Triton) and custom-accelerator (NKI/Pallas/TPU) ecosystems
- Background in compiler engineering, MLIR, or intermediate-representation lowering
- Deep understanding of memory-hierarchy optimization (shared-memory tiling, register pressure, bank conflicts, coalescing patterns)
- Contributions to kernel libraries such as cuBLAS, cuDNN, Triton community kernels, or JAX/XLA custom calls
This is a contractor role offering flexible, hourly engagement for experienced GPU engineers looking to contribute to impactful AI development work.
How to apply for this role
- Upload your resume — keep it up-to-date and in English. Mercor will auto-fill your profile from it.
- Complete the AI interview — a 15-minute conversation about your experience. Be ready to discuss specific projects and challenges you've solved.
- Submit your application — only about 20% of applicants finish all the steps, so completing yours puts you well ahead.