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Who this role suits
Working engineers who want flexible, project-based remote work that uses their coding and review skills. A strong portfolio or GitHub helps.
Full job description
Evaluate the quality, correctness, and hardware-appropriateness of Neuron Kernel Interface (NKI) development tasks used to train and evaluate a frontier AI lab's models. You'll assess CUDA→NKI migration fidelity, Trainium-specific performance-optimization quality, and cross-platform numerical-correctness standards — and provide clear, rubric-based written feedback.
Basic Qualifications • 2+ years of hands-on experience developing or optimizing kernels using the Neuron Kernel Interface (NKI) targeting AWS Trainium/Inferentia2 hardware • Strong understanding of NKI-specific development patterns: tile-based computation, SBUF/PSUM/HBM memory-hierarchy management, partition-dimension constraints, and DMA orchestration • Demonstrated experience assessing CUDA→NKI migration quality • Familiarity with Trainium-specific performance profiling (NeuronCore pipeline utilization, tensor-engine throughput, memory-bandwidth bottlenecks) • Experience defining or evaluating cross-platform numerical-correctness standards (GPU vs Trainium accumulation order, rounding behavior, mixed-precision semantics)
Preferred Qualifications • Direct experience with AWS Neuron SDK, Neuron Compiler internals, or contributions to NKI kernel libraries • Prior CUDA or Triton kernel development • Familiarity with Trainium hardware specifications (NeuronCore-v2 architecture, on-chip SRAM topology, supported data types: FP32/BF16/FP8/INT8) • Experience benchmarking ML training workloads on Trn1/Trn2 instances
Last checked open on Oct 28, 2026. Job description provided by Mercor.
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