Technical Program Head - Advanced Robotics Research at Siemens in Berkeley,California,United States of America

Job description

• Reports to: Global Head of Advanced Robotics Research.
• Scope: You own the robotics topic in the United States. You build and lead the California research team, set local technical direction, and coordinate with your peer Technical Program Heads in Munich and Shanghai under one global roadmap.
• Siemens is building a Long-Term Research organization with one mandate: create foundational technology that becomes Siemens business five or more years out. The robotics topic targets autonomous, polyfunctional robot systems: robots that generalize across tasks, embodiments, and industrial environments without per task reengineering.
• Three sites carry distinct capabilities: Munich, Shanghai, and California. You sit in the middle of the strongest robot learning ecosystem in the world, and your job is to make Siemens a serious research participant in it.
• Siemens brings what the frontier labs do not have: real factories, real safety requirements, real deployment surfaces, and the automation stack that runs a large share of global manufacturing. Your models will be validated on physical industrial testbeds and run on hardware prototyped in Shanghai. Fragmented prototypes are a failure condition of this program.
• You lead the California research program for Autonomous, Polyfunctional Robotics, with an initial focus on machine learning techniques for robots. You build and lead the team responsible for method development to enable robots to learn manipulation skills from human demonstrations using multi-modal robot foundation models, methods for the assurance of learned behavior, and the shared industrial benchmark, working with Munich and Shanghai under one global architecture and roadmap.

Responsibilities

• Robot foundation models and policy learning. Research on Robotics Foundation Models including but not limited to vision language action architectures, world models, cross embodiment policy transfer, action space representations, and embodiment conditioned adaptation.
• Learning from interaction for robotics. Create methods for robots to acquire and improve industrial skills through teleoperation, physical interaction, simulation, and autonomous experience. Establish data-generation and training approaches for coordinated use of arms, multi-fingered hands, torso, and locomotion, including manipulation while standing freely or moving under load.
• Safety and assurance of learned whole-body behavior. Create the safety concepts, verification methods, runtime monitoring, simulation evidence, contact limits, fall and recovery envelopes, and certification arguments required for learned behavior in shared industrial spaces. The research object is not generic robot safety, but the assurance challenge created by learned whole-body motion, locomotion, balance, and physical interaction.
• The robotics industrial benchmark. Lead the definition and evolution of a shared benchmark for industrial robotics work, including tasks, human baselines, acceptance metrics, trial protocols, and evidence requirements. Use the benchmark to measure whether a robot can perform work in different environments and to compare architectures, models, embodiments, and training approaches across the global program.
• One shared robotics architecture. Co-own the global architecture connecting the robot’s brain, body, hands, control, sensing, simulation, data schema, and evaluation harness. Define the intelligence-side interfaces and the shared action representation so that learning results can transfer across regional platforms without creating disconnected site prototypes.
• New topics will need to be defined as the field and Siemens strategy evolve; some directions above will be redefined and others stopped. You bring an open mind, fast moving and dynamic execution, and the combination of depth and agility to pivot when the evidence demands it, without losing coordination with the other sites or your grounding in the local research activities of your region.
• Local technical execution and research quality in California.
• Lead hiring and build the team: you recruit from a fiercely competitive market and hold a bar that survives it.
• Local partnerships with universities, frontier labs, and the startup ecosystem.
• Stay up to speed across every research direction in the topic and act as the authority on it inside Siemens.
• Engage in the research community through peer reviewed publications and presence at key events.
• Engage deeply in the local research activities of your region: universities, institutes, labs, and the surrounding ecosystem.
• Lead the filing of IP patents from your program: model architectures, data infrastructure, and adaptation methods are strategic assets.
• A transfer or venture hypothesis for your program’s results, developed with the global head.
• Cross site coordination with Munich and Shanghai so the program produces one system, not three prototypes.

Requirements

• PhD in robotics, control, machine learning, or a closely related field, or equivalent research depth demonstrated through sustained first tier output.
• Publication record at recognized venues (ICRA, IROS, CoRL, RSS, T-RO or comparable), including work you personally led.
• Hands on depth in at least two of: robot learning and foundation models for robotics, vision language action systems, large scale robot data collection and training infrastructure, sim to real transfer, manipulation.
• You have trained and evaluated real robot policies, not only published about them: a system exists because of your work.
• Led a research team, including hiring senior researchers and making kill decisions on your own projects.
• Experience with cross embodiment policy learning or multi robot data corpora such as Open X Embodiment.
• Experience building training infrastructure or data engines used by more than your own project.
• Contact rich or force controlled manipulation research.
• Open source leadership, benchmark creation, or standards influence in the robot learning community.
• Prior founder, founding team, or research lead experience at a robotics venture.

Skills

Required

• Vision language action systems
• Sim to real transfer
• Manipulation
• Leading research teams
• Hiring senior researchers
• Making kill decisions on projects

Preferred

• Cross embodiment policy learning
• Open source leadership
• Benchmark creation

Benefits

• A founding site leadership role in a new long horizon research organization with executive sponsorship and a protected mandate.
• Industrial deployment surfaces, factory scale validation, and automation platforms no frontier lab can replicate internally.
• A defined path from research results to venture incubation or transfer into Siemens businesses.
• Siemens offers a variety of health and wellness benefits to our employees. Details regarding our benefits can be found here: https://www.benefitsquickstart.com/siemens/index.html
• The pay range for this position is $203,000 - $348,000 annually with a target incentive of 20% of the base salary. The actual wage offered may be lower or higher depending on budget and candidate experience, knowledge, skills, qualifications, and premium geographic location.