We are seeking a **Lead Applied Scientist** to spearhead the creation of sophisticated robot learning policies that enable dexterous manipulation in practical settings. You will guide the end\-to\-end process of designing, training, and deploying models that fuse multimodal perception, policy formulation, and robotic execution. Join our collaborative AI Research team to advance embodied AI and tackle industrial\-scale autonomy and simulation challenges. Apply now to become a key player in pushing the frontier of robotics. **Responsibilities** * Develop, implement, and deploy cutting\-edge robot learning frameworks and policy architectures for manipulation and autonomous task sequencing that integrate multimodal sensory inputs, perception, planning, and execution using ROS/ROS2 * Build and refine comprehensive policy training pipelines, encompassing policy inference and closed\-loop control for both simulated and real\-world applications * Direct data management strategies for demonstrations, teleoperation, simulation workflows, and evaluation systems to guarantee reliable and safe deployment of robot policies * Partner with cross\-disciplinary teams and AI infrastructure specialists to deliver scalable, production\-level robotics solutions * Keep abreast of advancements in embodied AI research, contribute to reproducible studies, and disseminate knowledge through mentorship and technical talks * Enhance PyTorch implementations by creating custom modules and optimizing for peak performance and efficient deployment **Requirements** * PhD in a relevant STEM discipline or Master’s degree with equivalent professional experience in robotics or embodied AI * Minimum 5 years of hands\-on experience designing and deploying machine learning models on robotic platforms, including integration of multimodal sensory signals and control outputs * Comprehensive understanding of contemporary AI architectures such as Transformers and diffusion models * Advanced proficiency with PyTorch, including developing custom components and performance optimization * Practical expertise with Robot Operating System (ROS/ROS2\) and integrating policies into robotic control systems * Demonstrated contributions through publications, open\-source projects, or operational deployments * Capability to manage complex projects involving interdisciplinary collaboration * Excellent analytical, problem\-solving, and communication skills with English proficiency at C2 level **Nice to have** * Experience working with 3D computer vision, including perception and affordance modeling * Familiarity with simulation environments like Isaac Sim, Mujoco, Gazebo, or PyBullet * Expertise in dexterous manipulation and executing multi\-step autonomous tasks * Awareness of visual trends and their relevance to embodied AI applications * Experience adapting foundation models for embodied control and agents following instructions