Applied Scientist: Autonomous Robot Navigation

Amazon; San Francisco, Northern (CA, KY); Hybrid; USD 190,000 - 230,000; Full time

Job summary

Amazon is seeking a Scientist in Robot Navigation to architect and deliver navigation systems that are intelligent, safe, and scalable. You will blend learning-based planning with principled control, leveraging foundation models for embodied agents and MPC-based trajectory planning.

Join a world-class team building scalable autonomous robotics, collaborating across robotics, software, and product teams to deploy perception and navigation capabilities in dynamic real-world environments.

Qualifications

• 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, RL, neural motion planning).
• Strong experience with Model Predictive Control (MPC) and optimization-based planning.

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 to define and deliver perception capabilities
• Drive end-to-end ownership of ML models—from data collection and labeling to training, evaluation, and deployment
• Mentor junior scientists and engineers; contribute to a culture of technical excellence
• Define and track metrics to measure perception system performance in real-world environments
• Publish research findings in top-tier venues and contribute to patents

Skills

Python

C++

Java

ML for navigation

MPC

Research publication

Education

PhD in Robotics/CS/EE/Controls

Tools

PyTorch

JAX

ROS/ROS2

MuJoCo

Gazebo

Isaac Sim

Job description

Amazon is seeking a Scientist in Robot Navigation to architect and deliver navigation systems that are intelligent, safe, and scalable. You will blend learning-based planning with principled control, leveraging foundation models for embodied agents and MPC-based trajectory planning.

Join a world-class team building scalable autonomous robotics, collaborating across robotics, software, and product teams to deploy perception and navigation capabilities in dynamic real-world environments.