Sr. Applied Scientist - Computer Vision, Amazon Robotics

Amazon Robotics is at the forefront of developing advanced robotic systems for fulfillment centers worldwide. As a Senior Applied Scientist in Computer Vision, you'll be part of a team that builds high-performance, real-time robotic systems capable of perceiving, learning, and acting intelligently alongside humans at Amazon scale.

The role focuses on developing AI systems for robotics in fulfillment, enabling robots to interact safely and efficiently in high-density real-world environments. You'll work on cutting-edge problems in computer vision, deep learning, and robotics, including scene comprehension, large-scale generative models, closed-loop control, and robotic grasping.

Your responsibilities will include architecting and implementing machine learning models for vision systems, optimizing and deploying models at scale, and working closely with cross-functional teams. You'll contribute to both research and practical implementation, leading technical initiatives while mentoring other team members.

The position offers competitive compensation ranging from $150,400 to $260,000 per year, depending on location and experience. Amazon provides comprehensive benefits including medical, dental, vision coverage, parental leave, and a 401(k) plan.

This is an opportunity to solve unprecedented challenges in robotics and AI, working with state-of-the-art technology and contributing to systems that impact millions of customers. The ideal candidate combines technical expertise in computer vision and machine learning with strong leadership and communication skills.

You'll be part of Amazon Robotics, where you can make a significant impact on the future of automated fulfillment systems. The team values innovation, practical problem-solving, and the ability to turn research into real-world applications. If you're passionate about robotics, AI, and want to work on problems that haven't been solved at scale before, this role offers the perfect opportunity to make your mark in the field.