About the job
Join the Personal Robotics Group at Amazon, where you'll help pioneer intelligent robotic products that deliver meaningful customer experiences. As an Applied Scientist focused on Robot Navigation, you'll research and develop advanced navigation systems that enable robots to move reliably and safely through complex, dynamic environments. You'll work across a broad spectrum of navigation approaches—from classical methods to learning-based techniques and foundation models—to build robust solutions for autonomous robot navigation. In this role, you'll evaluate, adapt, and develop navigation methods that bridge the gap between state-of-the-art research and real-world deployment. You'll work closely with cross-functional teams to deliver integrated navigation capabilities that enable meaningful robot autonomy.
Responsibilities
Develop and implement robust navigation systems that enable reliable autonomous operation in complex, dynamic indoor environments with static and dynamic obstacles
Build simulation-based and on-device evaluation frameworks with comprehensive benchmarks and metrics for systematic comparison of navigation methods
Conduct sim-to-real transfer experiments, analyzing performance gaps and developing techniques to ensure reliable real-world navigation performance
Collaborate with perception, manipulation, and other teams to ensure seamless integration of navigation capabilities into the full robot system
Stay current with the latest advances in robot navigation, spatial reasoning, and related fields, and apply relevant findings to improve system performance
Qualifications
Minimum
PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
Experience with programming languages such as Python, Java, C++
Experience building machine learning models or developing algorithms for business application
Experience in patents or publications at top-tier peer-reviewed conferences or journals
Experience applying theoretical models in an applied environment
Experience developing and implementing deep learning models
Track record of solving complex technical problems
Preferred
Experience with sim-to-real transfer for navigation systems
Familiarity with visual navigation, semantic navigation, or foundation models applied to robot navigation
Experience evaluating and benchmarking multiple navigation approaches (classical, learning-based, foundation model-based)
Publication record at Robotics/ML/AI conferences (e.g., RSS, CoRL, ICRA, IROS, NeurIPS, ICML, ICLR)
1+ years of industry or academic research experience in robot navigation, motion planning, or autonomous systems
Experience spanning classical robot navigation (e.g., global/local path planning, MPC/trajectory optimization) and modern learning-based navigation techniques
Experience with simulation environments for robotics (Isaac Sim, Gazebo, or similar)