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Listed on Anyone AI since at least 24 Oct, the first day we read Anyone AI; last read 27 Oct.
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From the Anyone AI listing
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.
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