About the job
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.
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
Qualifications
Minimum
- 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
- 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