Job description

Our organization in Amazon Robotics builds robots that perform contact-rich manipulation 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 experienced Senior Applied Scientist to help guide a small team advancing reinforcement learning for manipulation. We are creating robots that learn how to push, flip, rearrange, and dexterously insert items with unparalleled robustness, speed, and reliability. Our goal is to deploy robots that will work across Amazon's global network and can handle the full diversity of items that Amazon sells. You will set the technical direction for how we learn these behaviors, from simulation training through reliable execution on physical robots, and you will demonstrate new manipulation capabilities on real hardware at scale. This team's mission reaches beyond any single product: to invent and apply manipulation capabilities that generalize to many future robotics applications. The robots our organization already deploys at scale give you a rare proving ground to collect data, run experiments, and get new policies onto real hardware faster than almost anywhere in the field. You will raise the bar for scientific rigor and engineering quality, and mentor other scientists as the team grows. Key job responsibilities - Set the technical direction for learning non-prehensile and contact-rich manipulation policies, from testing the latest advances in the field through demonstrated capability on hardware. - Oversee the development of reinforcement learning approaches that address the long tail of diverse, demanding manipulation conditions. - Own the path from simulation training to reliable, real-time execution on physical robots, making evidence based calls on where learned approaches should replace engineered ones. - Demonstrate new manipulation capabilities on real robots at scale, and turn one-off results into repeatable methods. - Establish the standards, evaluation practices, and data-informed improvement loops that the team builds on. - Mentor scientists and engineers, and raise the bar for applied science rigor and engineering quality. - Partner across control, perception, and hardware to integrate learned behaviors into working systems. - Represent Amazon in academia through publications and scientific presentations. A day in the life Amazon offers a full range of benefits that support 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. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply - PhD or equivalent research experience - 7+ years of applied research experience - 3+ years of building machine learning models for business application experience - Experience programming in Java, C++, Python or related language - Track record of training reinforcement learning or imitation learning policies in simulation and successfully transferring them to physical systems - 3+ years of building and deploying learning-based control on robotic systems - Experience leading technical projects and mentoring scientists or engineers - Experience with sim-to-real transfer at scale (domain randomization, system identification, etc) - Experience designing reward functions and training curricula for reinforcement learning on robotic systems - Experience with contact-rich or non-prehensile manipulation, including extrinsic dexterity and force/torque control - Publications in top robotics or machine learning venues (RSS, CoRL, ICRA, NeurIPS, ICML, etc.) 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 amazon.jobs 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 amazon.jobs . USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually

Requirements

Must-haves

Key job responsibilities
Set the technical direction for learning non-prehensile and contact-rich manipulation policies, from testing the latest advances in the field through demonstrated capability on hardware.
Oversee the development of reinforcement learning approaches that address the long tail of diverse, demanding manipulation conditions.
Own the path from simulation training to reliable, real-time execution on physical robots, making evidence based calls on where learned approaches should replace engineered ones.
Demonstrate new manipulation capabilities on real robots at scale, and turn one-off results into repeatable methods.
Establish the standards, evaluation practices, and data-informed improvement loops that the team builds on.
Mentor scientists and engineers, and raise the bar for applied science rigor and engineering quality.
Partner across control, perception, and hardware to integrate learned behaviors into working systems.
Represent Amazon in academia through publications and scientific presentations.
A day in the life
PhD or equivalent research experience
7+ years of applied research experience
3+ years of building machine learning models for business application experience
Experience programming in Java, C++, Python or related language
Track record of training reinforcement learning or imitation learning policies in simulation and successfully transferring them to physical systems
3+ years of building and deploying learning-based control on robotic systems
Experience leading technical projects and mentoring scientists or engineers
Experience with sim-to-real transfer at scale (domain randomization, system identification, etc)
Experience designing reward functions and training curricula for reinforcement learning on robotic systems
Experience with contact-rich or non-prehensile manipulation, including extrinsic dexterity and force/torque control
Publications in top robotics or machine learning venues (RSS, CoRL, ICRA, NeurIPS, ICML, etc.)
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningjavapython

Platform: amazon
Effort: medium

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401(K)

Pto

Paid Time Off

Parental Leave