Staff ML Engineer – AWS Trainium & SageMaker

📋 Description

• Train and operate machine learning models using Amazon SageMaker with AWS Trainium as the
• Develop and optimize PyTorch training workloads for execution on AWS Trainium.
• Analyze how PyTorch code compiles and executes across Trainium and NeuronCore architecture.
• Optimize memory utilization, throughput, and other performance characteristics of training
• Diagnose training failures and performance issues caused by hardware, compiler behavior, device
• Distinguish model- and data-level problems from accelerator- and compiler-level issues during

🎯 Requirements

• Strong hands-on experience with PyTorch, including a deep understanding of training workflows.
• Experience with distributed or multi-device model training is highly desirable.
• Production experience using Amazon SageMaker for model training and/or inference.
• Strong understanding of how machine learning workloads interact with accelerator hardware and
• Ability to debug issues that originate at the hardware, accelerator, compiler, or runtime layer
• Experience optimizing training workloads for performance, throughput, memory utilization, or cost.

🎁 Benefits

• Opportunity to work on production AI systems using AWS Trainium and Amazon SageMaker.
• Exposure to custom AI accelerator hardware, compiler behavior, distributed training, and advanced
• Hands-on work with complex machine learning workloads for enterprise clients.
• Direct collaboration with client and internal engineering teams.
• Opportunity to solve specialized technical problems that extend beyond conventional ML application
• Production-focused engineering environment emphasizing systems that ship and operate at scale.