AWS Trainium / NKI Kernel Expert

Anyone AI is recruiting experienced AWS Trainium / Neuron Kernel Interface (NKI) engineers for a specialized project focused on evaluating and improving kernel development tasks for AI workloads.

We’re looking for engineers with hands-on experience building or optimizing NKI kernels on AWS Trainium or Inferentia2 hardware who understand how Trainium’s architecture differs from traditional GPU programming.

What You’ll Work On

You’ll review and evaluate technical tasks involving:

NKI kernel correctness and Trainium-specific development patterns

CUDA → NKI kernel migrations

Trainium performance optimization and benchmarking

Memory management across SBUF, PSUM, and HBM

Tile-based computation and DMA scheduling

Cross-platform numerical correctness between CUDA/Triton and NKI

Trainium-specific performance bottlenecks and optimization opportunities

Technical feedback and quality assessment of kernel implementations

The work involves determining whether implementations are not only technically correct, but also idiomatic and optimized for Trainium hardware rather than simply translated from GPU-based approaches.

What We’re Looking For

2+ years of hands-on experience developing or optimizing kernels with the Neuron Kernel Interface (NKI)

Experience working with AWS Trainium and/or Inferentia2

Strong understanding of:

Tile-based computation

SBUF / PSUM / HBM memory hierarchy

Partition dimension constraints

DMA orchestration

Trainium-specific optimization techniques

Ability to evaluate CUDA → NKI migrations

Experience profiling and optimizing workloads on Trainium

Understanding of numerical differences across GPU and Trainium backends

Strong ability to analyze complex technical implementations and provide clear written feedback

Nice to Have

Experience with the AWS Neuron SDK or Neuron Compiler

CUDA or Triton kernel development experience

Knowledge of NeuronCore-v2 architecture

Experience with FP32, BF16, FP8, and INT8 workloads

Experience benchmarking workloads on Trn1 or Trn2 instances

Familiarity with nki.language , @nki.jit , or XLA custom calls

Experience with technical evaluation, AI/ML data projects, RLHF, or rubric-based assessment

Engagement

• Work Type: Remote
• Engagement: Part-time, project-based consulting
• Focus: AWS Trainium / NKI kernel engineering and technical evaluation

This is a strong fit for engineers who have worked deeply with AWS Trainium infrastructure and low-level ML kernel optimization and are interested in applying that expertise to technically challenging AI projects.