Applied Scientist - Amazon Robotics, Amazon Robotics - Vulcan Stow

About the role

Our team in Amazon Robotics builds robotic systems that perform contact‑rich manipulation tasks safely and reliably in complex, unstructured environments at Amazon scale. We push the boundaries of robotic manipulation to handle enormous object diversity, leveraging deep expertise in planning, control, perception, and machine learning. As an Applied Scientist on the Motion Behaviors team, you will develop learned controllers and manipulation behaviors, applying reinforcement learning and behavior cloning to robots operating in Amazon fulfillment centers. You will decide when learned approaches should replace engineered solutions, select behaviors based on estimated risk, and collaborate across disciplines using rich operational data to continuously improve system performance.

Responsibilities

• Develop learned controllers and manipulation behaviors, from research prototyping through deployment on production robots.
• Research, design, and implement motion planning, control, and decision‑making algorithms that improve the performance of deployed systems.
• Design and deploy learning pipelines that take policies from simulation training to reliable, real‑time execution on physical robots.
• Develop models that predict manipulation outcomes and inform behavior selection under uncertainty.
• Leverage operational data from deployed systems to systematically identify failure modes and drive policy improvements.
• Represent Amazon in academia through publications and scientific presentations.

Qualifications

Basic Qualifications

• PhD, or Master’s degree and 4+ years of experience in science, technology, engineering, or a related field.
• Experience programming in Java, C++, Python, or a related language.
• Experience with patents or publications at top‑tier peer‑reviewed conferences or journals.
• Experience training reinforcement learning or imitation learning policies for manipulation or robot control problems.
• Strong background in reinforcement learning, including reward design and training at scale in simulation.

Preferred Qualifications

• Experience in professional software development.
• Experience with sim‑to‑real transfer (domain randomization, system identification, etc.), including transferring learned policies onto physical robots.
• Familiarity with learned dynamics or world models for manipulation.

Benefits

• Medical, dental, and vision coverage.
• Maternity and parental leave options.
• Paid time off (PTO).
• 401(k) plan with matching.
• Comprehensive health insurance (including prescription, basic life & AD&D, supplemental life options, EAP, mental health support, medical advice line, flexible spending accounts, adoption and surrogacy reimbursement).
• Additional benefits such as adoption and surrogacy reimbursement, flexible spending accounts, and employee assistance programs.

Pay

Base salary range: $142,800 – $193,200 USD annually (USA, WA, Seattle). The total compensation package includes sign‑on payments and restricted stock units (RSUs), with final compensation determined by experience, qualifications, and location.