Do you want to create intelligent, adaptable robots with global impact? At Amazon Robotics, our collaborative team builds high-performance, real-time robotic systems that perceive, plan, and act intelligently alongside humans—at Amazon scale. Our mission is to enable robots to interact safely, efficiently, and fluently through the complex challenges of a company that sells everything, everywhere. We target high-impact algorithmic unlocks in areas such as scene and activity understanding, large-scale generative models, closed-loop control, robotic grasping and manipulation—all of which have high-value impact for our current and future fulfillment networks. Our solutions enable robots to learn from their own experiences, from each other, and from humans to build intelligence that feeds itself. We are seeking an Applied Scientist to join our Motion Planning and Control team creating a fleet of manipulation robots at scale. You will apply the latest trends in research to solve real-world problems alongside a world-class team of experts in motion planning, computer vision, deep learning, intelligent control, and semi-supervised and unsupervised learning. We target high-impact algorithmic unlocks in areas such as scene and activity understanding, closed-loop control, robotic grasping, and manipulation in high-contact environments. Your contributions will drive the development and scaling of advanced robotic systems capable of handling complex, dynamic environments and transforming how robots interact with the world around them. Key job responsibilities • Research, design, implement and evaluate complex motion planning and control algorithms that improve the performance of systems deployed in real-world conditions. • Create experiments and prototype implementations of new learning algorithms and prediction techniques. • Work closely with software engineering team members to drive scalable, real-time implementations. • Collaborate with perception, machine learning and robotic controls experts to implement and deploy algorithms, such as machine learning models. • Collaborate closely with hardware engineering team members on developing systems from prototyping to production level. • Represent Amazon in academia community through publications and scientific presentations. • Work with stakeholders across hardware, science, and operations teams to iterate on systems design and implementation.