Applied Scientist, Navigation

About the role

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.

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

Requirements

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

Preferred Qualifications

• 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, 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