AmazonPosted 2w ago

Applied Science Manager, Fleet Autonomy, Amazon Robotics

Applied Science Manager, Fleet Autonomy, Amazon Robotics at Amazon scores 91 out of 100 on AI centrality, which makes it a Level 4 role on this board.

US, MA, N.readingseniorfull-time$184k-$249k

AI in this role

computer-vision

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.

Basic qualifications

- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- 5+ years of industry or academic research experience
- 2+ years of scientists or machine learning engineers management experience
- Experience programming in Java, C++, Python or related language
- Experience building machine learning models or developing algorithms for business application
- Strong understanding of autonomous mobile robots and how they navigate
- Experience deploying robotic systems into the field

Preferred qualifications

- Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers
- Experience with mobile robot fleets in industrial or logistics settings
- Experience developing computationally efficient decision making algorithms for live production systems

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, MA, N.Reading - 183,800.00 - 248,700.00 USD annually

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Computer Vision

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Computer Vision. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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