Senior AI/ML Distributed Training Engineer (PyTorch)

Amazon Web Services (AWS); Seattle (WA); On-site; USD 168,100 - 227,400; Full time

Benefits offered by this job

Health insurance

RSUs

Paid time off

Mentorship

Job summary

Annapurna Labs (U.S.) Inc. is seeking a Senior Machine Learning Engineer to join the Distribute Training team on AWS Neuron, focusing on distributed training for Trainium and Inferentia. You will build and optimize training support for large models using PyTorch, enabling efficient scaling on AWS silicon.

You will collaborate with chip architects and runtime engineers to push performance, implement training strategies, and mentor teammates as part of a fast-moving ML software stack at AWS.

Qualifications

• Bachelor's degree in computer science or equivalent.
• 5+ years of non-internship professional software development experience.
• 5+ years of programming with at least one major language.
• 5+ years of design/architecture leadership experience.
• 5+ years of full software development life cycle experience.
• Experience as a mentor or tech lead.
• Experience in machine learning and large-scale training with LLMs, including PyTorch.

Responsibilities

• Lead efforts to build distributed training support into PyTorch, Neuron compiler, and runtime stacks.
• Enable distributed training strategies and optimize models for peak performance on AWS custom silicon.
• Collaborate with cross-functional teams including chip architects and runtime engineers.

Skills

Distributed ML

PyTorch

Mentor/Tech-Lead

LLM Training

Performance Tuning

Education

Bachelor's degree in CS or equivalent

Master's degree in CS (preferred)

Tools

PyTorch/Torch libraries

JAX

TensorFlow

Job description

Annapurna Labs (U.S.) Inc. is seeking a Senior Machine Learning Engineer to join the Distribute Training team on AWS Neuron, focusing on distributed training for Trainium and Inferentia. You will build and optimize training support for large models using PyTorch, enabling efficient scaling on AWS silicon.

You will collaborate with chip architects and runtime engineers to push performance, implement training strategies, and mentor teammates as part of a fast-moving ML software stack at AWS.