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)
- 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-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
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, CA, SAN FRANCISCO - 192,200.00 - 260,000.00 USD annually
