Remote - Canada
Actively hiring, first seen by us 3 days agoListed location: Remote - CanadaRead the full posting
Train and operate models on SageMaker with Trainium as the compute
Write and optimize PyTorch training code for Trainium's NeuronCore and compiler
Diagnose hardware-specific training issues versus data or code problems
Turn requests into working, cost-aware end-to-end training pipelines
Tune distributed training runs for throughput and cost on SageMaker
Train and operate models on Amazon SageMaker with AWS Trainium as the underlying compute
Write and optimize PyTorch training code with a real understanding of how it compiles and executes on Trainium
Diagnose training run issues that show up specifically because of the hardware, not just the model
Translate a request for "a Trainium job" into an actual working, cost-aware training pipeline, end to end
Tune distributed training runs for throughput and cost on SageMaker's training infrastructure
Strong, hands-on PyTorch experience, ideally including distributed or multi-device training
Production experience with Amazon SageMaker for training and/or inference
you understand device-specific compilation and can debug issues that are actually about the accelerator, not just the model
Solid Python fundamentals
comfort operating in a client-facing, production engineering environment
Actively hiringFirst seen by us 3 days ago, still listed, and not a repost of an earlier posting.
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Day 3
Early. Similar jobs usually come down between day 6 and day 71.
At Robots and Pencils, jobs usually come down by about day 14.
From 4,617 similar postings that came down, and 6 of Robots and Pencils’s. Coming down is not always a hire.
9 open jobs
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- AWS Trainium or Inferentia (Neuron SDK) experience is a strong plus; if you don't have it yet but have deep PyTorch and a track record of picking up new hardware targets fast, we want to talk to you - Solid Python fundamentals and comfort operating in a client-facing, production engineering environment
Robots & Pencils is an AWS Partner building production AI systems for enterprise clients who need real engineering, not proofs of concept that never ship. We work forward-deployed, embedded directly with client teams, solving the problems that are too new or too specialized for a typical vendor relationship to handle. The Role We're looking for an engineer who can operate and train models on Amazon SageMaker running on AWS Trainium, AWS's custom silicon built specifically for large-scale model training. This isn't a role where you call an API and wait. You'll be walking up the stack: understanding what a training request actually looks like at the Trainium hardware and compiler level, then carrying that understanding all the way up through PyTorch training code and into a production SageMaker pipeline. PyTorch is the backbone of this work. If you know the framework deeply and you're comfortable reasoning about how your code actually behaves on custom accelerator hardware rather than treating it as a black box, this role is built around that skill set specifically.
Robots & Pencils is an AWS Partner building production AI systems for enterprise clients who need real engineering, not proofs of concept that never ship. We work forward-deployed, embedded directly with client teams, solving the problems that are too new or too specialized for a typical vendor relationship to handle. The Role We're looking for an engineer who can operate and train models on Amazon SageMaker running on AWS Trainium, AWS's custom silicon built specifically for large-scale model training. This isn't a role where you call an API and wait. You'll be walking up the stack: understanding what a training request actually looks like at the Trainium hardware and compiler level, then carrying that understanding all the way up through PyTorch training code and into a production SageMaker pipeline. PyTorch is the backbone of this work. If you know the framework deeply and you're comfortable reasoning about how your code actually behaves on custom accelerator hardware rather than treating it as a black box, this role is built around that skill set specifically. What You'll Do
What You'll Do - Train and operate models on Amazon SageMaker with AWS Trainium as the underlying compute - Write and optimize PyTorch training code with a real understanding of how it compiles and executes on Trainium (NeuronCore architecture, compiler behavior, memory and throughput tradeoffs)
- Comfort working close to the hardware layer: you understand device-specific compilation and can debug issues that are actually about the accelerator, not just the model - AWS Trainium or Inferentia (Neuron SDK) experience is a strong plus; if you don't have it yet but have deep PyTorch and a track record of picking up new hardware targets fast, we want to talk to you - Solid Python fundamentals and comfort operating in a client-facing, production engineering environment
- Translate a request for "a Trainium job" into an actual working, cost-aware training pipeline, end to end - Tune distributed training runs for throughput and cost on SageMaker's training infrastructure - Work directly with client and internal engineering teams to scope and deliver real production training workloads, not experiments that stay in a notebook
- 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: you understand device-specific compilation and can debug issues that are actually about the accelerator, not just the model
- Train and operate models on Amazon SageMaker with AWS Trainium as the underlying compute - Write and optimize PyTorch training code with a real understanding of how it compiles and executes on Trainium (NeuronCore architecture, compiler behavior, memory and throughput tradeoffs) - Diagnose training run issues that show up specifically because of the hardware, not just the model, distinguishing a data or code problem from a compiler or device-level one
What You'll Bring - Strong, hands-on PyTorch experience, ideally including distributed or multi-device training - Production experience with Amazon SageMaker for training and/or inference
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New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
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We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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Remote - Canada
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