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

The ARI-SciCom Engineering team builds and operates the software platform that powers scientific publishing and research collaboration across Amazon. Our tools support the full lifecycle of scientific communication - from paper authoring and approval through conference management and research impact assessment and an innovator database containing hundreds of thousands of researchers, their institutions, and their projects. Our tools accelerate innovation for a community of 10K+ Amazon scientists and 20K+ research collaborators.

We sit at the intersection of engineering, science, and AI: Building the infrastructure that enables Amazon's scientists to discover, share, and amplify their work. The team is trusted with both operational excellence on mission-critical systems and bold innovation on next-generation AI tooling.

The team currently owns and operates the following production systems:

• Publications Approval Pipeline: End-to-end tooling for routing scientific papers and data sets through Amazon's internal review and approval workflow, ensuring compliance, quality, and publication readiness.
• Conference Management Platform: Software supporting internal conferences including reviewer assignment, paper submission, peer review orchestration, and program committee decision-making workflows.
• Amazon Science Repository: A searchable repository of papers and presentations authored by Amazon scientists for both internal meetings (AMLC, CSS, many others) and external conferences and journals, enabling discovery and cross-team collaboration.
• Custom Academic Research Salesforce Instance: An external portal for call-for-proposal mechanisms, such as Amazon Research Awards and an innovator database that catalogs global researchers, their projects, and their institutions.

Forward-Looking Roadmap. In addition to maintaining and evolving the current portfolio, the team is building the next generation of science infrastructure at Amazon. The incoming SDM will own and accelerate this roadmap:

• AI-Powered Review Tools: Intelligent systems for paper and proposal reviewing, providing automated feedback to authors and supporting reviewers with AI-assisted evaluation. Built on large language models and integrated with existing internal and external submission workflows.
• Amazon Science Directory: A comprehensive directory of Amazon's ~10,000 scientists, including expertise profiles, publication histories, research domains, and collaboration networks - enabling discovery, mentorship, and team formation across org boundaries.
• Research Impact & Outcomes Tracking: Tools supporting the Amazon Research Initiative (ARI) team in tracing and evaluating the outcomes of Amazon-funded university research, connecting funded projects to publications, software artifacts, patents, and business impact.

Key job responsibilities

We are seeking an experienced Software Development Manager or previous Principal Engineer to lead the ARI-SciCom Engineering team. You will own the technical vision and execution for a portfolio of tools that directly enable scientific discovery and collaboration at Amazon scale.

This role requires a leader who can balance operational excellence on existing systems with ambitious product development on AI-powered tools that will transform how Amazon supports its rapidly evolving science community. You will manage a team of software development engineers, set technical direction, drive roadmap prioritization, and partner closely with science leadership, and internal conference organizers.

You will be expected to think big about how AI and automation can accelerate scientific productivity while delivering incremental value through disciplined execution and bar-raising engineering standards.

About the team

The Academic Research Initiatives (ARI)-Science Community (SciCom) team supports Amazon's investments in internal science capabilities and external academic research. We believe that external research only generates value when it flows back into Amazon, and internal Amazon science only reaches its fullest potential when connected to the frontier of academic discovery.