Senior 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

- 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)

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