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.
Sobre a vaga
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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