Research Scientist - Embodied AI at Confidential Berkeley, CA

Research Scientist – Embodied AI In this role you will work on-site in our Berkeley, CA office. The Challenge Architect robotic foundation models and lead the design of large‑scale robot learning frameworks tailored for industrial manipulation and autonomous planning. Build full‑stack training pipelines for robotics applications integrating multi‑modal inputs and ensuring reproducibility. Advance data collection strategies to support model training and validation. Bridge the sim-to-real gap for embodied AI systems utilizing simulation frameworks. Create scalable inference loops that combine perception with real‑time motion control and on‑robot deployment. Work alongside cross‑functional teams to turn research prototypes into deployable robotics systems that are robust and safe. Present and communicate ideas and project results to internal and external project partners and at conferences. Required Qualifications Ph.D. or M.Sc. in Computer Science, Electrical Engineering, Robotics, or similar from an accredited university. A proven track record of contributions at top‑tier robotics and AI venues such as IROS, ICRA, RSS, CoRL, etc. Professional‑level fluency in C++ and Python. Proof experience in building and deploying machine learning models on robotic systems, including training, evaluation, and integration. Deep technical knowledge of modern model architectures, specifically Transformers, VLAs/VLMs, and Diffusion Models, with experience in large‑scale training. Hands‑on experience implementing, debugging, and deploying software on physical robotic platforms. Solid understanding of embedded systems, hardware/software integration, and OS fundamentals. Proficiency in English, both written and verbal. Legally authorized to work in the United States without corporate sponsorship now or in the future. Preferred skills & experience Expertise in 3D scene understanding, multimodal grounding, and sensor fusion. Familiarity with simulation environments such as Isaac Sim, Mujoco, or Gazebo, and demonstrated experience with sim‑to‑real transfer strategies. Working knowledge of robotics middleware (ROS/ROS2) and integration of machine learning components into real‑time robotic stacks. Experience in building or adapting foundation models for embodied tasks (RFMs, VLMs/VLAs, multimodal diffusion). Experience with cloud computing environments. We value your unique identity and perspective. We are fully committed to providing equitable opportunities and building a workplace that reflects the diversity of society, while ensuring that we attract the best talent based on qualifications, skills, and experiences. #J-18808-Ljbffr Ellenco Estágios e Treinamentos