Sr. Applied Scientist, Fauna

About the role

We are seeking a Sr. Applied Scientist to focus on Robot Navigation. In this role, 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.

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 world model, 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
• Mentor fellow scientists and engineers while maintaining strong individual technical contributions

Requirements

• PhD, or Master's degree and 6+ years of applied research experience
• 3+ years of industry or academic research experience
• Strong publication record at major Robotics/ML/AI conferences (e.g., RSS, CoRL, ICRA, IROS, NeurIPS, ICML, ICLR)
• Experience programming in Python, C++, or related language
• Experience with sim-to-real transfer for robotic systems
• Demonstrated track record of leading technical projects
• Experience mentoring junior scientists/engineers

Qualifications

• History of impactful first-author publications at major conferences
• Experience bridging research with practical engineering implementation in robotics systems
• Experience 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)