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Amazon Industrial Robotics is seeking a Sr. Applied Scientist in Robot Perception to push the boundaries of perception in real-world robotic systems.
You will design, implement, and deploy state-of-the-art perception algorithms, bridging computer vision, 3D sensing, and language-model concepts to enable safe human-robot collaboration. You will lead cross-functional teams, mentor engineers, and contribute to research publications and patents while scaling perception solutions across Amazon’s
Job ID: 10454082 | Amazon.com Services LLC
Amazon Industrial Robotics is on a mission to redefine the future of automation — and we're looking for exceptional talent to help lead the way. We are building the next generation of advanced robotic systems that seamlessly blend cutting‑edge AI, sophisticated control systems, and novel mechanical design to create adaptable, intelligent automation solutions capable of operating safely alongside humans in dynamic, real‑world environments. At Amazon Industrial Robotics, we leverage the power of machine learning, artificial intelligence, and advanced robotics to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence — and we're just getting started. As a Sr. Applied Scientist in Robot Perception, you will be at the forefront of this transformation. You will develop and deploy state‑of‑the‑art perception algorithms that enable robots to truly understand and interact with the physical world — bridging the gap between theoretical research and realworld impact. Bringing deep expertise in Computer Vision and a nuanced understanding of the capabilities and limitations of modern Vision‑Language Models (VLMs), you will innovate boldly and push the boundaries of what's possible. Our vision for the Perception layer is ambitious: to enable seamless, intelligent interaction between the user, the robot, and its environment. This is a rare opportunity to work at the intersection of deep learning, large language models, and robotics — contributing to research that doesn't just advance the field, but reshapes it. You will collaborate with world‑class teams pioneering breakthroughs in dexterous manipulation, locomotion, and human‑robot interaction, all at an unprecedented scale.
Our Industrial Robotics Group is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Veterans, military spouses, and people with disabilities are encouraged to apply.
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, CA, SAN FRANCISCO - 192,200.00 - 260,000.00 USD annually