Senior AI/ML Distributed Training Engineer

Amazon Web Services (AWS)

Seattle (WA)

On-site

USD 168,000 - 227,000

Full time

2 days ago
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Benefits offered by this job

Health insurance
401(k) matching
Paid time off
Parental leave

Job summary

Annapurna Labs (U.S.) Inc. in Seattle is hiring a software engineer to join the Distributed Training team for AWS Neuron.

The role focuses on developing, enabling, and tuning distributed training across large model families and multi-modal generation models on Trainium servers. You will work with PyTorch, JAX, and the Neuron compiler/runtime to maximize training throughput, model FLOPs utilization, and efficiency, collaborating with chip architects and runtime engineers to remove bottlenecks and

Qualifications

  • 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 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, and operations experience.
  • Experience as a mentor, tech lead or leading an engineering team.

Responsibilities

  • Optimize distributed training performance on Trainium across PyTorch, JAX, and Neuron stack.
  • Enable and tune large-scale training workloads on the latest Trainium instances.

Skills

Software development experience
Programming experience
Design/architecture leadership
Software development lifecycle
Mentorship / tech lead

Education

Bachelor's degree in computer science

Job description

Annapurna Labs (U.S.) Inc. in Seattle is hiring a software engineer to join the Distributed Training team for AWS Neuron.

The role focuses on developing, enabling, and tuning distributed training across large model families and multi-modal generation models on Trainium servers. You will work with PyTorch, JAX, and the Neuron compiler/runtime to maximize training throughput, model FLOPs utilization, and efficiency, collaborating with chip architects and runtime engineers to remove bottlenecks and

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