Applied Scientist, Navigation

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.

As a 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 an 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. Your vision is 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.

• 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.

• 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.

Our team 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.

• 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, or equivalent).
• Proven track record of translating research into deployed systems.

• Experience applying foundation models or large pre‑trained models to robotics tasks (navigation, manipulation, or embodied AI).
• Familiarity with world models, visual navigation, or vision‑language action models.
• Experience with sim‑to‑real transfer and high‑fidelity simulation environments (Isaac Sim, Mu Jo Co , Gazebo).
• Knowledge of SLAM, localization, and mapping systems.
• Experience with ROS/ROS2 and real‑time robotics middleware.
• Hands‑on…