Senior AI/ML Distributed Training Engineer

Annapurna Labs (U.S.) Inc.; Cupertino (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Annapurna Labs (U.S.) Inc. is seeking a Senior Machine Learning Engineer to join the AWS Neuron distributed training team. You will contribute to development, enablement, and performance tuning for large ML models, including GPT and other LLMs, on Trainium and Inferentia.

You'll work with chip architects, compilers, and runtime engineers to implement training support in PyTorch/JAX via XLA, and optimize for peak hardware efficiency.

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 software programming language experience
• 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
• 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing

Responsibilities

• Lead efforts to build distributed training support into PyTorch and JAX using XLA, the Neuron compiler, and runtime stacks.
• You will optimize models to achieve peak performance and maximize efficiency on AWS Trainium/Inferentia hardware.
• Collaborate with chip architects, compiler engineers and runtime engineers to create, build and tune distributed training solutions.

Skills

Python

Distributed training

PyTorch

JAX

Software development

System design

Education

Bachelor's degree in computer science or equivalent

Tools

XLA

Neuron compiler

Deepspeed

Nemo

PyTorch

JAX

Job description

Annapurna Labs (U.S.) Inc. is seeking a Senior Machine Learning Engineer to join the AWS Neuron distributed training team. You will contribute to development, enablement, and performance tuning for large ML models, including GPT and other LLMs, on Trainium and Inferentia.

You'll work with chip architects, compilers, and runtime engineers to implement training support in PyTorch/JAX via XLA, and optimize for peak hardware efficiency.