Applied Scientist, Amazon Robotics - Vulcan

Description

Our team in Amazon Robotics builds robotic systems that perform contact-rich manipulation tasks safely and reliably in complex, unstructured environments — at Amazon scale. Our scientists and engineers push the boundaries of robotic manipulation to handle enormous object diversity, bringing deep expertise across planning, control, perception, and machine learning. We learn from real-world data at a scale that few teams in robotics can access. We are seeking an Applied Scientist to join our Motion Behaviors team.

Key job responsibilities

• Design, implement, and deploy motion planning, control, and manipulation algorithms on production robotic systems.
• Partner with experts across disciplines including perception, hardware and software to create intelligent, integrated systems and solutions.
• Contribute to the development of learned manipulation behaviors and controllers, including sim-to-real deployment.
• Write production-quality code and own scalable, real-time implementations.
• Validate algorithms on hardware, iterating between simulation and real-world testing to ensure robust performance.
• Analyze experimental results, identify failure modes, and drive systematic improvements to system performance.
• Champion Amazon in academia through publications and scientific presentations.

A day in the life

Amazon offers a full range of benefits for you and eligible family members, including domestic partners. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment.

• Medical, Dental, and Vision Coverage
• Maternity and Parental Leave Options
• Paid Time Off (PTO)
• 401(k) Plan

Basic Qualifications

• PhD, or Master's degree and 4+ years of science, technology, engineering or related field experience
• Experience programming in Java, C++, Python or related language
• Experience with robotics work cells and control systems
• Strong background in motion planning, control theory (compliant control, trajectory optimization), or learning-based manipulation

Preferred Qualifications

• Experience deploying and supporting complex robotic systems at scale
• Familiarity with reinforcement learning, behavior cloning, and/or sim-to-real transfer for manipulation
• Experience with contact-rich manipulation, force/torque control, and/or constrained motion planning
• Publications in top robotics, controls, or machine learning venues (RSS, CoRL, ICRA, IROS, NeurIPS, ICML, etc.)