Staff ML Engineer – AWS Trainium & SageMaker
robotsandpencils· Engineering
About this role
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
- 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
- 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
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
- 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
Frequently Asked Questions
Is the salary disclosed for the Staff ML Engineer – AWS Trainium & SageMaker position at robotsandpencils?
The salary for this Staff ML Engineer – AWS Trainium & SageMaker role at robotsandpencils is not publicly listed. Click "Apply Now" to learn more about the compensation package on their official careers page.
Is the Staff ML Engineer – AWS Trainium & SageMaker job at robotsandpencils remote?
Yes, this Staff ML Engineer – AWS Trainium & SageMaker position at robotsandpencils is remote, with team members based in US Remote. You can work from home or anywhere in the supported regions.
Which team or department does the Staff ML Engineer – AWS Trainium & SageMaker at robotsandpencils belong to?
This Staff ML Engineer – AWS Trainium & SageMaker position is part of the Engineering department at robotsandpencils. See the full job description for more information about the team structure and responsibilities.
How do I apply for the Staff ML Engineer – AWS Trainium & SageMaker position at robotsandpencils?
Click the "Apply Now" button on this page. You will be redirected to robotsandpencils's official application portal hosted on greenhouse where you can submit your application directly.
When was the Staff ML Engineer – AWS Trainium & SageMaker job at robotsandpencils posted?
This Staff ML Engineer – AWS Trainium & SageMaker position at robotsandpencils was posted on Sep 16, 2026. Apply as soon as possible — early applications are often reviewed first.
Staff ML Engineer – AWS Trainium & SageMaker
robotsandpencils
You'll be redirected to robotsandpencils's official application page on Greenhouse.