VP of Robotics Research – Remote (Worldwide)

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This is a fully remote position, open to candidates based in 55 countries, including United States, United Kingdom, Canada, Germany, India, France, Netherlands, Singapore.

Job Title

VP of Robotics Research

Job Type

Full-time

Location

Remote

The Role

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

Our research focuses on four connected areas

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.

You will set research direction across these areas, stay close to models and physical systems, build a world-class research team, and partner with leading AI and robotics labs on their hardest Physical AI problems.

What You’ll Do

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.

What We’re Looking For

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.

Preferred Qualifications

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 foundation models, or large-scale imitation and reinforcement learning.

Hands-on experience with robotic platforms and frameworks such as ROS/ROS2, Isaac Lab, MuJoCo, ManiSkill, or equivalent systems.

Experience building robotics benchmarks, simulation environments, teleoperation systems, safety evaluations, or physical-world data engines.

Experience building agents for engineering, scientific, manufacturing, or laboratory workflows.

Compensation & Benefits Notice

The national pay range for this full-time position is base salary of $250,000 –$350,000. All employees are eligible for equity compensation, and employees may also receive performance-based bonuses, dependent on role and subject to company policies. The company provides a comprehensive benefits package, including up to 100% reimbursement for health-insurance premiums, paid time