Robots & Pencils Verified
Software Development, Digital Agency, Consulting, IT Services
Staff ML Engineer – AWS Trainium & SageMaker
RemoteRemoteFull TimeStaffPosted 10 days ago
Required skills
- production AI systems
- SageMaker
- Trainium
- PyTorch
- training code
- accelerator hardware
- SageMaker pipeline
- distributed training
- cost-aware
- production training workloads
Nice to have
- AWS Trainium
- Inferentia
- Neuron SDK
- deep PyTorch
Role summary
Robots & Pencils is seeking a Staff ML Engineer to build and operate production AI systems for enterprise clients, focusing on training models using AWS Trainium and Amazon SageMaker. This role requires deep PyTorch expertise, comfort with hardware-level compilation and debugging, and the ability to translate model training requests into cost-aware, production-ready SageMaker pipelines. You will work closely with client and internal teams to deliver specialized AI solutions, requiring strong Python fundamentals and a production engineering mindset.
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 RoleWe'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
Similar roles
- Staff Engineer / AI Builder - NYCRobots & Pencils · New York, NY · Onsite
- Staff ML Data ScientistRobots & Pencils · Remote · Remote
- ML Engineer-DatabricksJade Biosciences, Inc. · San Jose, CA · Onsite
Senior AI/ML EngineerUnitedHealth Group · Remote · Remote
AI/ML EngineerBooz Allen Hamilton · San Diego, CA · Hybrid- PBiosignal ML Engineer / Data ScientistPreload · San Francisco, CA · Onsite
