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Job description
Role Overview
Evaluate Neuron Kernel Interface, NKI, development tasks used to train and assess advanced AI models. You will review CUDA-to-NKI migrations, Trainium-focused performance optimizations, and cross-platform numerical correctness, then deliver clear written feedback using defined rubrics.
Key Responsibilities
Assess NKI kernel-development tasks for quality, correctness, and suitability for AWS Trainium and Inferentia2 hardware.
Evaluate the fidelity of CUDA-to-NKI migrations.
Review Trainium-specific performance optimization quality.
Evaluate numerical-correctness standards across GPU and Trainium platforms.
Provide clear, rubric-based written feedback.
Qualifications
At least 2 years of hands-on experience developing or optimizing NKI kernels for AWS Trainium or Inferentia2 hardware.
Strong knowledge of tile-based computation, SBUF, PSUM, and HBM memory-hierarchy management, partition-dimension constraints, and DMA orchestration.
Experience evaluating CUDA-to-NKI migration quality.
Familiarity with Trainium performance profiling, including NeuronCore pipeline utilization, tensor-engine throughput, and memory-bandwidth bottlenecks.
Experience establishing or assessing cross-platform numerical-correctness standards, including GPU versus Trainium accumulation order, rounding behavior, and mixed-precision semantics.
Preferred Qualifications
Experience with the AWS Neuron SDK, Neuron Compiler internals, or NKI kernel-library contributions.
Prior CUDA or Triton kernel-development experience.
Familiarity with NeuronCore-v2 architecture, on-chip SRAM topology, and FP32, BF16, FP8, and INT8 data types.
Experience benchmarking machine-learning training workloads on Trn1 or Trn2 instances.
Work Terms
Remote role, open to candidates located in the United States.
Hourly engagement.
Compensation
$70 to $90 per hour.
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.