AWS Trainium Kernel Engineer

Job not on LinkedIn

🕒 Yesterday

🗽 New York – Remote

🗽 New York – Remote

💵 $60 - $80 / hour

⏳ Contract/Temporary

🟢 Junior

🟡 Mid-level

👷🏻‍♀️ Engineer

🚫👨‍🎓 No degree required

👻 Ghost score 0%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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24-MAG

WebsiteLinkedInAll Job Openings

2 - 10 employees

🤝 B2B

💼 Consulting

B2B • Consulting

24-MAG is a commercial strategy and execution firm that helps B2B organizations design and implement systems, workflows, and operating rhythms for sales, client management, and cross-functional projects. They focus on transforming scattered processes into aligned, measurable, and scalable commercial functions—covering pipeline structure, account management frameworks, and operational discipline for teams seeking efficient, intentional growth.

📋 Description

• Evaluate kernels developed using the Neuron Kernel Interface (NKI) • Assess whether implementations appropriately target AWS Trainium and Inferentia2 hardware • Review low-level computation patterns for technical correctness • Review migrations of existing CUDA kernels to NKI • Assess tile decomposition, computation strategies, and partitioning decisions • Review use of SBUF, PSUM, and HBM across Trainium memory hierarchies • Evaluate DMA orchestration, data movement, sequencing, stalls, and synchronisation • Review Trainium-specific optimisation strategies and identify performance bottlenecks • Evaluate NeuronCore pipeline utilisation, tensor-engine throughput, and memory-bandwidth behaviour • Review numerical consistency between GPU and Trainium implementations • Assess precision choices, mixed-precision behaviour, numerical stability, and supported formats • Evaluate implementations using the AWS Neuron SDK, compiler, and NKI kernel libraries • Review benchmark results for Trn1 or Trn2 workloads and assess experimental methodology • Assess assigned kernel-development tasks against structured technical criteria • Provide clear written, rubric-based technical feedback supported by implementation, profiling, or numerical evidence

🎯 Requirements

• 2+ years of hands-on experience developing or optimising kernels using the Neuron Kernel Interface (NKI) • Professional experience targeting AWS Trainium or Inferentia2 hardware • Strong understanding of tile-based computation • Deep familiarity with SBUF, PSUM, and HBM memory management • Strong knowledge of partition-dimension constraints and DMA orchestration • Demonstrated experience evaluating or performing CUDA-to-NKI migrations • Familiarity with Trainium-specific performance profiling • Experience assessing NeuronCore pipeline utilisation, tensor-engine throughput, and memory-bandwidth bottlenecks • Strong understanding of cross-platform numerical correctness and mixed-precision behaviour • Strong written communication and ability to provide precise technical feedback • Direct experience with the AWS Neuron SDK, Neuron Compiler internals, or NKI kernel libraries is preferred • Prior CUDA or Triton kernel development experience is advantageous • Familiarity with NeuronCore-v2 architecture and supported numerical formats is preferred • Experience benchmarking ML workloads on Trn1 or Trn2 instances is advantageous • H1-B and STEM OPT support is unavailable for this engagement

🏖️ Benefits

• Part-time independent contractor engagement • Fully remote within the United States • Flexible scheduling based on project requirements • Projects may be extended, shortened, or concluded based on project needs and performance