GPU Kernel Expert Mercor · Remote — United States $70-90/hr →

Dorado; Northern (KY); Hybrid; USD 100,000 - 180,000; Full time

Job summary

Dorado is seeking a scientist-engineer to evaluate GPU/accelerator kernel development tasks used to train and evaluate frontier AI models. You will assess numerical correctness, performance benchmarks, task scoping, and runtime validity across diverse kernel types.

You will deliver rubric-based written feedback, identify failure modes, and help shape best practices for future kernel work in a collaborative research environment.

Qualifications

• 3+ years hands-on kernel development or verification.
• Experience with CUDA, Triton, NKI, or Pallas (JAX).
• Familiarity with runtime failures (OOM, driver issues, shape/stride).

Responsibilities

• Evaluate kernel tasks for correctness, performance, and completeness.
• Provide rubric-based feedback on scope, accuracy, and quality.
• Identify and document compilation/runtime failures across frameworks.
• Recommend improvements and best practices for kernel development.

Skills

Numerical correctness

Performance profiling

Kernel development

Tools

CUDA

Triton

NKI

Pallas (JAX)

Job description

Evaluate the quality, correctness, and completeness of GPU/accelerator kernel development tasks used to train and evaluate a frontier AI lab's models. You'll assess numerical correctness, performance-benchmarking fairness, task scoping, and compilation/runtime validity across diverse kernel task types — and provide clear, rubric-based written feedback.

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 for kernels (absolute/relative/ULP tolerances, reference-implementation selection)
• Demonstrated experience with performance profiling and benchmarking (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, migration between hardware targets, 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
• Understanding of memory-hierarchy optimization (shared-memory tiling, register pressure, bank conflicts, coalescing patterns)
• Contributions to kernel libraries (cuBLAS, cuDNN, Triton community kernels, JAX/XLA custom calls)