Staff ML Engineer – AWS Trainium, SageMaker
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Job Description
Staff ML Engineer building production PyTorch training pipelines on AWS Trainium and SageMaker. Delivering cost-aware, hardware-optimized AI systems for enterprise clients.
Responsibilities:
- Train and operate models on Amazon SageMaker with AWS Trainium as the underlying compute
- Write and optimize PyTorch training code for Trainium, including reasoning about NeuronCore architecture, compiler behavior, memory, and throughput tradeoffs
- Diagnose hardware-specific training run issues and distinguish data or code problems from compiler- or device-level problems
- Translate Trainium job requests into working, cost-aware end-to-end training pipelines
- Tune distributed training runs for throughput and cost on SageMaker training 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, including device-specific compilation and accelerator-level debugging
- 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 may substitute for Trainium experience
- Solid Python fundamentals
- Comfort operating in a client-facing, production engineering environment
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