VP of Robotics Research

• Job Title: VP of Robotics Research
• Job Type: Contractor
• Location: Remote

About the hiring company:

Our client is a rapidly growing, venture-backed AI company helping shape the next generation of intelligent systems. By combining world-class human expertise with advanced machine learning workflows, they enable leading AI organizations to build, evaluate, and improve cutting-edge models used across a wide range of industries.

The company works with highly accomplished professionals in fields such as software engineering, finance, healthcare, legal, operations, research, and other specialized domains. These experts contribute directly to the development of advanced AI systems by providing real-world knowledge, evaluations, feedback, and domain-specific judgment that help models reason more accurately and perform more effectively.

Leveraging a proprietary AI-driven talent assessment and matching platform, the organization identifies exceptional professionals globally and connects them with high-impact projects at the forefront of artificial intelligence.

Backed by more than $40 million in funding and supported by a rapidly expanding international network of experts, the company is building critical human intelligence infrastructure for the AI economy and creating meaningful opportunities for professionals to apply their expertise in entirely new ways.

Job Summary:

The Role

As VP of Robotics Research, 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:

1. Physical Agents & Robotics — building agents that perceive, reason, plan, and act through robots, machines, instruments, and other physical systems.
2. Learning & Data — studying how demonstrations, trajectories, sensor data, simulation, feedback, and autonomous experience improve physical-world intelligence.
3. Evaluation & Generalization — developing rigorous methods to measure physical reasoning, manipulation, planning, robustness, safety, and transfer across tasks and environments.
4. 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

1. Set the Physical AI research agenda. Identify the highest-leverage problems across robotics, embodied agents, learning, data, evaluation, and physical-world automation.
2. Build agents that act in the physical world. Develop systems that connect foundation models and agents to robots, machines, instruments, and other physical interfaces.
3. Advance general-purpose robot learning. Lead research across vision-language-action models, imitation learning, reinforcement learning, world models, and multimodal policies.
4. Develop a science of physical-world data. Study which demonstrations, trajectories, sensor streams, environments, and supervision signals best improve learning and generalization.
5. 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.
6. Advance evaluation and generalization. Build rigorous benchmarks for manipulation, spatial reasoning, planning, tool use, robustness, safety, and transfer across tasks and embodiments.
7. Bridge simulation and reality. Study how simulated and real-world experience should be combined and where sim-to-real transfer breaks down.
8. Advance physical-world R&D automation. Build agents that operate across scientific and engineering workflows, including simulation, experimentation, design, fabrication, and laboratory systems.
9. Build research that compounds. Create reusable datasets, environments, simulators, hardware testbeds, evaluation infrastructure, and research tools.
10. 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

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

Preferred Qualifications

1. Master’s or PhD in Robotics, Computer Science, AI, Machine Learning, Engineering, or a related field.
2. Research experience at a leading AI, robotics, or embodied intelligence organization.
3. Strong publication record at venues such as CoRL, RSS, ICRA, IROS, NeurIPS, ICML, or CVPR, or equivalent high-impact technical work.
4. Experience with generalist robot policies, cross-embodiment learning, robot foundation models, or large-scale imitation and reinforcement learning.
5. Hands-on experience with robotic platforms and frameworks such as ROS/ROS2, Isaac Lab, MuJoCo, ManiSkill, or equivalent systems.
6. Experience building robotics benchmarks, simulation environments, teleoperation systems, safety evaluations, or physical-world data engines.
7. 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. We provides a comprehensive benefits package, including up to 100% reimbursement for health-insurance premiums, paid time off, a 401(K) plan with a company match, and additional benefits designed to support a high-performing, remote-first workforce.

Equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable local laws, regulations and ordinances.

Our hiring process utilizes artificial intelligence tools to assist in candidate screening and assessment. Our AI tools are designed to complement, not replace, human decision-making.

A very attractive and competitive package is offered.