Trainium Kernel Expert - AI Trainer

Mercor; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Mercor seeks an experienced kernel-evaluation engineer to assess NKI development tasks for frontier AI models. You will judge CUDA→NKI migration fidelity, Trainium-specific performance quality, and cross-platform numerical-correctness, delivering rubric-based feedback.

Applicants should have 2+ years in NKI kernel work, with knowledge of tile-based computation, SBUF/PSUM/HBM memory management, and DMA orchestration, plus familiarity with AWS Neuron SDK and Neuron Compiler internals.

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).

Responsibilities

• Evaluate the quality, correctness, and hardware-appropriateness of NKI development tasks used to train and evaluate a frontier AI lab's models.
• Assess CUDA→NKI migration fidelity, Trainium-specific performance-optimization quality, and cross-platform numerical-correctness standards.
• Provide clear, rubric-based written feedback.

Skills

NKI kernel development

CUDA→NKI migration

Trainium performance profiling

Cross-platform numerical-correctness

DMA orchestration

Tools

AWS Neuron SDK

Neuron Compiler internals

Triton kernel development

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