Staff ML Engineer – AWS Trainium, SageMaker
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Job Description
Staff ML Engineer training and optimizing PyTorch models on AWS Trainium and SageMaker. Delivering cost-aware production AI pipelines for enterprise clients.
Responsibilities:
- Train and operate models on Amazon SageMaker with AWS Trainium as the underlying compute
- Write and optimize PyTorch training code, including understanding of NeuronCore architecture, compiler behavior, memory, and throughput tradeoffs
- Diagnose training run issues caused by hardware, distinguishing data or code problems from compiler- or device-level issues
- Translate Trainium job requests into working, cost-aware end-to-end training pipelines
- Tune distributed training runs for throughput and cost on SageMaker infrastructure
- Work directly with client and internal engineering teams to scope and deliver production training workloads
Requirements:
- Strong, hands-on PyTorch experience, ideally including distributed or multi-device training
- Production experience with Amazon SageMaker for training and/or inference
- Comfort working close to the hardware layer; understanding of device-specific compilation and ability to debug accelerator-related issues
- AWS Trainium or Inferentia (Neuron SDK) experience is a strong plus
- Deep PyTorch experience and a track record of picking up new hardware targets quickly if lacking Trainium/Inferentia experience
- Solid Python fundamentals
- Comfort operating in a client-facing, production engineering environment
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