Applied Scientist, Navigation

Amazon is on a mission to redefine the future of automation. 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. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence.

About the role

As a Scientist in Robot Navigation, you will architect and deliver 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 in-depth knowledge of control-theoretic approaches such as model predictive control (MPC)-based trajectory planning. Your work will bridge data-driven intelligence with principled control-theoretic guarantees, enabling robots to move fluidly and safely through dynamic environments by understanding context, anticipating change, and adapting in real time.

You will lead research that connects cutting-edge academic advances with production-grade deployment, collaborating with world-class teams in robotic autonomy, manipulation, and human-robot interaction.

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
• 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
• Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives to help peers grow and succeed
• Maintain significant hands-on contribution to technical solutions while mentoring team members

About the team

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.

Requirements

• 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 programming in Java, C++, Python, or related language

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

Pay

• USA, CA, San Francisco: $171,600.00 - $222,200.00 USD annually
• USA, CA, Sunnyvale: $171,600.00 - $222,200.00 USD annually
• USA, MA, North Reading: $142,800.00 - $193,200.00 USD annually

Benefits

Amazon offers comprehensive benefits including:

• Health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance, and optional supplemental life plans)
• Employee Assistance Program (EAP) and Mental Health Support
• Medical Advice Line
• Flexible Spending Accounts
• Adoption and Surrogacy Reimbursement coverage
• 401(k) matching
• Paid time off and parental leave
• Sign-on payments and restricted stock units (RSUs)