Sr Mgr, Amazon Science Engineering, Academic Research Initiatives & Science Community

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

The ARI-SciCom Engineering team builds and operates the software platform that powers scientific publishing and research collaboration across Amazon. The role involves leading a team of software development engineers to own the technical vision and execution for a portfolio of tools that directly enable scientific discovery and collaboration at Amazon scale, with a focus on AI-powered tools for reviewing and a comprehensive directory of scientists.

What you'd actually do

  1. Own the technical vision and execution for a portfolio of tools that directly enable scientific discovery and collaboration at Amazon scale.
  2. Manage a team of software development engineers, set technical direction, drive roadmap prioritization, and partner closely with science leadership, and internal conference organizers.
  3. Think big about how AI and automation can accelerate scientific productivity while delivering incremental value through disciplined execution and bar-raising engineering standards.
  4. Build the next generation of science infrastructure at Amazon, including AI-Powered Review Tools and a Research Impact & Outcomes Tracking system.

Skills

Required

  • 10+ years of engineering experience
  • 5+ years of engineering team management experience
  • Experience managing multiple concurrent programs, projects and development teams in an Agile environment
  • Experience leading the design, build and deployment of complex and performant (reliable and scalable) software solutions in production
  • Proficiency with AI development methodologies, modern CI/CD practices, and engineering operational excellence.

Nice to have

  • Hands-on experience building AI/ML-powered products or integrating large language models into production workflows.
  • Familiarity with scientific publishing workflows, academic conference management, or research administration systems.
  • Demonstrated ability to work effectively with non-engineering stakeholders — scientists, researchers, and program managers — translating ambiguous domain needs into clear engineering requirements.

What the JD emphasized

  • AI-Powered Review Tools
  • large language models
  • AI and automation can accelerate scientific productivity

Other signals

  • AI-Powered Review Tools
  • large language models
  • AI and automation can accelerate scientific productivity