Senior ML Engineer - Sustainability Platform & Innovation

Amazon

San Francisco (CA)

On-site

USD 193,000 - 262,000

Full time

3 days ago
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Benefits offered by this job

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

Job summary

Amazon.com Services LLC is seeking an experienced software engineer to build data and ML infrastructure for sustainability. You will work with scientists to automate training/evaluation, and to expose models via reliable interfaces while maintaining flexibility for researchers.

You will develop AI-assisted workflows and reusable tools across research and production. The role emphasizes scalable design, platform reliability, and collaboration with applied scientists to deliver production-grade

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 as a mentor, tech lead or leading an engineering team.

Responsibilities

  • Build reusable data and ML infrastructure and infrastructure-as-code components for data processing, model training, and inference.
  • Create tools for repeatable experiments and model comparisons with preserved data, code, and configuration.
  • Develop and evaluate AI-assisted workflows and tooling for coding agents with automated checks and review.
  • Support adoption and evolution by documenting trade-offs and maintaining compatibility as components evolve.

Skills

Software development
Architecture leadership
Mentorship / Tech lead
Experimentation tooling
CI/CD & Ops

Education

Bachelor's degree in computer science or equivalent

Job description

Amazon.com Services LLC is seeking an experienced software engineer to build data and ML infrastructure for sustainability. You will work with scientists to automate training/evaluation, and to expose models via reliable interfaces while maintaining flexibility for researchers.

You will develop AI-assisted workflows and reusable tools across research and production. The role emphasizes scalable design, platform reliability, and collaboration with applied scientists to deliver production-grade

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