Research Scientist – Embodied AI

Siemens•Berkeley, CA

2d•$104,887 - $179,806•Onsite

About The Position

In this role you will work on-site in our Berkeley, CA office. We are looking for a Research Scientist who will advance how intelligent robotic systems perceive, act, and adapt in the physical world. In our research group, we are pioneering the next generation of embodied AI, integrating multimodal foundation models, robot learning architectures, and real-world deployment to solve manipulation, planning, and simulation challenges in industrial applications. We work in collaboration with our business units, leading academic institutions and government agencies.

Requirements

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
Proven 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.

Nice To Haves

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

Responsibilities

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