Staff ML Engineer – AWS Trainium, SageMaker

🕒 September 16

🇺🇸 United States – Remote

⏰ Full Time

🔴 Lead

☁️ Cloud Engineer

👻 Ghost score 17%

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.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

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, 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