Software Development Engineer, ML Infrastructure Team

Amazon Amazon · Big Tech · Seattle, WA · Software Development

Software Development Engineer II for the ML Infrastructure team at AWS, focusing on building and optimizing platforms for ML and HPC technologies. The role involves developing CI/CD systems, orchestrating GPU clusters, creating performance dashboards, and implementing AI-powered automation to ensure the reliable and performant delivery of ML networking software and support new EC2 instance types.

What you'd actually do

  1. Build and maintain infrastructure that monitors and reports on functionality and performance of massive testing workloads run at scale across multiple GPU instance types.
  2. Write Python code that orchestrates large clusters, runs benchmarks and ML applications across a matrix of instance types, operating systems, and software stack versions.
  3. Use AWS Managed Grafana and Athena to digest performance data and build dashboards that catch functional and performance regressions before they reach customers.
  4. Build automation using LLMs to analyze test failures and surface actionable insights to developers.
  5. Contribute to cross-team readiness for new instance type launches by delivering performance data that shapes go/no-go decisions.

Skills

Required

  • 3+ years of non-internship professional software development experience
  • 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • Experience programming with at least one software programming language

Nice to have

  • 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
  • Bachelor's degree in computer science or equivalent
  • Experience working with Linux
  • Experience with AWS Services including EC2, Lambda, S3, DynamoDB, SQS
  • Experience coding in Python
  • Experience with TypeScript and AWS CDK for infrastructure as code

What the JD emphasized

  • performance data
  • ML networking software
  • GPU instance types
  • ML Infrastructure
  • AI at scale

Other signals

  • ML Infrastructure
  • performance data
  • GPU clusters
  • ML networking software