Trainium Kernel Expert & AI Training Evaluator

Obsidian; San Francisco (CA); On-site; USD 140,000 - 180,000; Full time

Job summary

Obsidian evaluates Neuron Kernel Interface (NKI) development tasks used for frontier AI model training and evaluation. The role focuses on assessing CUDA→NKI migration fidelity, Trainium-specific performance optimization quality, and cross-platform numerical-correctness standards.

The candidate will deliver rubric-based feedback with concrete recommendations to ensure hardware-appropriate kernel implementations across AWS Trainium/Inferentia2 hardware.

Qualifications

• 2+ years 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)

Responsibilities

• Evaluate CUDA→NKI migration fidelity
• Assess Trainium-specific performance optimization quality
• Define or apply cross-platform numerical-correctness standards
• Provide rubric-based written feedback and concrete recommendations

Skills

NKI kernel development

CUDA→NKI migration

Trainium profiling

Memory hierarchy management

DMA orchestration

Tools

AWS Neuron SDK

Neuron Compiler

NKI kernel libraries

Job description

Obsidian evaluates Neuron Kernel Interface (NKI) development tasks used for frontier AI model training and evaluation. The role focuses on assessing CUDA→NKI migration fidelity, Trainium-specific performance optimization quality, and cross-platform numerical-correctness standards.

The candidate will deliver rubric-based feedback with concrete recommendations to ensure hardware-appropriate kernel implementations across AWS Trainium/Inferentia2 hardware.