Specialists assess Neuron Kernel Interface tasks, checking CUDA-to-NKI migrations, Trainium performance and cross-platform numerical behavior. It suits engineers with two or more years optimizing kernels on AWS Trainium or Inferentia who can explain accumulation and rounding effects.
About the role
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
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