VP of Robotics Research

As VP of Robotics Research at micro1, you will define and lead our research agenda for Physical AI - intelligent systems that can understand, reason about, and act in the physical world.

VP of Robotics Research

Full-time

Remote

• Physical Agents & Robotics - building agents that perceive, reason, plan, and act through robots, machines, instruments, and other physical systems.
• Learning & Data - studying how demonstrations, trajectories, sensor data, simulation, feedback, and autonomous experience improve physical-world intelligence.
• Evaluation & Generalization - developing rigorous methods to measure physical reasoning, manipulation, planning, robustness, safety, and transfer across tasks and environments.
• Physical-World R&D Automation - exploring how agents can accelerate scientific and engineering workflows through simulation, experimentation, design, fabrication, and real-world tools.

• Set the Physical AI research agenda. Identify the highest-leverage problems across robotics, embodied agents, learning, data, evaluation, and physical-world automation.
• Build agents that act in the physical world. Develop systems that connect foundation models and agents to robots, machines, instruments, and other physical interfaces.
• Advance general-purpose robot learning. Lead research across vision-language-action models, imitation learning, reinforcement learning, world models, and multimodal policies.
• Develop a science of physical-world data. Study which demonstrations, trajectories, sensor streams, environments, and supervision signals best improve learning and generalization.
• Build scalable data-generation systems. Develop methods for generating high-quality interaction data through human demonstration, teleoperation, autonomous rollout, simulation, and hybrid human agent workflows.
• Advance evaluation and generalization. Build rigorous benchmarks for manipulation, spatial reasoning, planning, tool use, robustness, safety, and transfer across tasks and embodiments.
• Bridge simulation and reality. Study how simulated and real-world experience should be combined and where sim-to-real transfer breaks down.
• Advance physical-world R&D automation. Build agents that operate across scientific and engineering workflows, including simulation, experimentation, design, fabrication, and laboratory systems.
• Build research that compounds. Create reusable datasets, environments, simulators, hardware testbeds, evaluation infrastructure, and research tools.
• Build and lead the research team. Recruit and develop exceptional researchers and engineers, partner with leading labs, and publish consequential research.

• Deep Physical AI expertise. Significant experience in robotics, embodied AI, robot learning, autonomous systems, or related fields.
• Strong learning and agentic systems intuition. Deep understanding of modern robot learning, multimodal models, imitation learning, reinforcement learning, world models, planning, and control.
• Physical systems intuition. Strong understanding of sensing, calibration, control, contact, actuation, latency, uncertainty, and hardware constraints.
• Exceptional research taste. You identify problems that can meaningfully expand what AI systems can do in the physical world.
• Experimental rigor. You turn open questions into strong hypotheses, controlled experiments, meaningful baselines, and evidence-backed conclusions.
• Strong evaluation judgment. You understand generalization, distribution shift, benchmark design, hardware-specific effects, and the failure modes of physical AI evaluations.
• Systems thinking. You reason across models, agents, data, hardware, simulation, tools, and evaluation as parts of a single system.
• Technical range. You are comfortable moving between model training, agent design, simulation, evaluation infrastructure, and physical experimentation.
• Research leadership. You have experience setting direction, mentoring exceptional technical talent, and leading ambitious research programs under uncertainty.

• Master's or PhD in Robotics, Computer Science, AI, Machine Learning, Engineering, or a related field.
• Research experience at a leading AI, robotics, or embodied intelligence organization.
• Strong publication record at venues such as CoRL, RSS, ICRA, IROS, NeurIPS, ICML, or CVPR, or equivalent high-impact technical work.
• Experience with generalist robot policies, cross-embodiment learning, robot…