Applied Science Manager, Fleet Autonomy, Amazon Robotics

We are seeking an Applied Science Manager to lead the team that invents how our autonomous mobile robot fleet thinks - from how robots negotiate shared space to adapting fleet behavior as tasks and deadlines change. This is a rare opportunity to own the evolution of multi-robot coordination in free space at an unprecedented scale.

Proteus is Amazon"s first fully autonomous mobile robot and navigates freely alongside people in our fulfillment centers, perceiving its environment and making decisions in real time. The fleet is growing quickly in scale, diversity, and intelligence, with thousands of robots deployed and new platforms including mobile manipulation entering the mix. The science challenge is making a diverse, heterogeneous fleet perform reliably in a world that changes constantly - where priorities shift hour to hour, new capabilities come online, and operational conditions create new constraints.

Your team will own algorithms at multiple layers of this system: how groups of robots autonomously coordinate through intersections and corridors, how fleet traffic is shaped across the road network, which tasks are assigned to which robots and when, and a new fleet orchestration layer that interacts directly with Operations to take in requests, collaborate to solve complex operational challenges, and adapt fleet performance and behavior on the fly.

Whether you"ve been leading a research team in industry and want a bigger canvas, or you"ve been driving impactful academic research in close partnership with industry and are ready to make the leap, this is a role where your ideas become fleets of robots, and those robots deliver for hundreds of millions of customers.

Key job responsibilities

• Set the technical vision and research agenda; identify the right problems and sequence bets across near-term production needs and long-horizon research
• Build, hire, and develop a team of applied scientists; provide scientific mentorship and grow careers
• Drive algorithms from research through production deployment in partnership with engineering
• Represent the team externally through publications, academic collaborations, and community engagement

A day in the life

Your internal stakeholders are engineering teams who productionize your algorithms, operations leaders whose buildings depend on fleet performance, and peer scientists building learned models of fleet dynamics. You spend your time shaping research direction, unblocking technical challenges, reviewing experimental results, and partnering across disciplines to get science into production.

About the team

You would join a multi-disciplinary science organization with deep expertise in planning, optimization, machine learning, and robotics - working at the frontier of multi-robot coordination where scale, diversity, and dynamism intersect. Our algorithms ship to real robots within months of conception, and we learn from the operational data they generate. We maintain active academic collaborations and contribute to the research community through publications, workshops, and open problems.