Applied Scientist, Navigation

Description Amazon 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, 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.

Scientist in Robot Navigation, you will be at the forefront of this transformation — architecting and delivering navigation systems that are intelligent, safe, and scalable. You will bring deep expertise in learning-based planning and control, a strong understanding of foundation models and their application to embodied agents, and as well as have in-depth understanding of control-theoretic approaches such as model predictive control (MPC)-based trajectory planning.

You will develop navigation solutions that seamlessly blend data-driven intelligence with principled control-theoretic guarantees. Our vision is bold: to build navigation systems that allow robots to move fluidly and safely through dynamic environments — understanding context, anticipating change, and adapting in real time. You will lead research that bridges the gap between cutting-edge academic advances and production grade deployment, collaborating with world-class teams pushing the boundaries of robotic autonomy, manipulation, and human-robot interaction.

Join us in building the next generation of intelligent navigation systems that will define the future of autonomous robotics at scale.

Key job responsibilities

• Design, develop, and deploy perception algorithms for robotics systems, including object detection, segmentation, tracking, depth estimation, and scene understanding
• Lead research initiatives in computer vision, sensor fusion and 3D perception
• Collaborate with cross-functional teams including robotics engineers, software engineers, and product managers to define and deliver perception capabilities
• Drive end-to-end ownership of ML models — from data collection and labeling strategy to training, evaluation, and deployment
• Mentor junior scientists and engineers; contribute to a culture of technical excellence
• Define and track key metrics to measure perception system performance in real-world environments
• Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents

A day in the life

• Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment
• Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations
• Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team
• Mentor team members while maintaining significant hands-on contribution to technical solutions

About The Team

• Our team is a 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.

Basic Qualifications

• Experience programming in Java, C++, Python or related language
• PhD in Robotics, Computer Science, Electrical Engineering, Controls, or a related field
• 2+ years of experience in robot navigation, motion planning, or autonomous systems
• Deep expertise in learning-based approaches to navigation (e.g., imitation learning, reinforcement learning, neural motion planning, diffusion-based policies)
• Strong experience with Model Predictive Control (MPC) and optimization-based planning (PyTorch, JAX,…