Staff ML Engineer – AWS Trainium, SageMaker

Robots & Pencils

. Train and operate models on Amazon SageMaker with AWS Trainium as the underlying compute .

Posted 11/2/2026full-timeRemote • CanadaLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in training and optimizing models using Amazon SageMaker and PyTorch, with a strong focus on hardware-specific performance tuning and client-facing production engineering. Proficient in diagnosing and resolving issues related to distributed training and device-level compilation.

Highest-signal resume keywords

PyTorch ExperienceAmazon SageMaker Production ExperienceAWS Trainium ExperienceDistributed Training OptimizationPython Fundamentals

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Model TrainingPyTorch OptimizationDistributed TrainingHardware-Level DebuggingCost-Aware Pipeline DevelopmentNeuronCore Architecture UnderstandingCompiler Behavior AnalysisThroughput TuningData Problem DiagnosisDevice-Specific Compilation

Soft Skills

Client-Facing CommunicationCollaboration with Engineering Teams

Tools & Technologies

Amazon SageMakerAWS TrainiumNeuron SDKMulti-Device Training

Industry Keywords

Production EngineeringTraining WorkloadsCost Optimization

Tech Stack

Tools & technologies

AWSPythonPyTorch

About the role

Key responsibilities & impact

Train and operate models on Amazon SageMaker with AWS Trainium as the underlying compute
Write and optimize PyTorch training code, including understanding NeuronCore architecture, compiler behavior, and memory/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 infrastructure
Work directly with client and internal engineering teams to scope and deliver production training workloads

Requirements

What you’ll need

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 quickly learning new hardware targets if Trainium or Inferentia experience is not already held
Solid Python fundamentals
Comfort operating in a client-facing, production engineering environment