Staff ML Engineer – AWS Trainium, SageMaker

🔥 18 hours ago

🇨🇦 Canada – Remote

⏰ Full Time

🔴 Lead

☁️ Cloud Engineer

👻 Ghost score 13%

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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Robots & Pencils

WebsiteLinkedInAll Job Openings

51 - 200 employees

🤖 Artificial Intelligence

🤝 B2B

☁️ SaaS

💰 Venture Round on 2022-04

Artificial Intelligence • B2B • SaaS

Robots & Pencils is a digital innovation firm that specializes in developing digital strategies and products utilizing mobile, web, and frontier technologies to drive transformation for businesses. They focus on areas such as mobile development, product design, UX research, artificial intelligence, machine learning, and organizational change management. With a commitment to blending creativity with technology, they aim to unlock data insights and enhance product innovation, ultimately delivering excellent customer experiences and helping brands gain a competitive edge.

📋 Description

• 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