About the job
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. 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.
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
Qualifications
Minimum
Experience programming in Java, C++, Python or related language
Have publications at top-tier peer-reviewed conferences or journals
PhD in Robotics, Computer Science, Electrical Engineering, Controls, or a related field
5+ 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
Preferred
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-languageaction models
Experience with sim-to-real transfer and high-fidelity simulation environments (Isaac Sim, MuJoCo, Gazebo)
Knowledge of SLAM, localization, and mapping systems
Experience with ROS/ROS2 and real-time robotics middleware
Hands-on experience deploying navigation systems on physical robots in dynamic, real-world environments
Experience with safety-critical systems and formal verification of learned controllers
Familiarity with multi-agent coordination and fleet-level navigation