Job Description

This is a staff leadership role at micro1, where the VP of Robotics Research sets the research direction for Physical AI: systems able to understand, reason about and act in the real world.

The leader decides which problems deserve attention across robot agents, how physical-world data improves learning, how generalization is measured, and how agents can speed up scientific and engineering work. The role stays close to models and hardware while growing a research team and working with outside AI and robotics labs on difficult problems.

Robotics expertise matters to physical AI because sensing, contact, latency and hardware limits decide whether a model's behavior holds up outside simulation. The position is full-time and remote.

This opportunity is with micro1, an AI research and data company that engages domain experts to train and evaluate AI systems. micro1 hires for it and runs the work, and Apply opens micro1's own application page.

Key Responsibilities

Define the Physical AI research agenda by choosing the highest-impact open problems in robotics and embodied intelligence.

Build agents that link foundation models to robots, instruments and other physical systems.

Lead research on generalist robot learning, including VLA models, imitation learning, reinforcement learning, world models and multimodal policies.

Study which demonstrations, trajectories, sensor streams and supervision signals most improve learning and generalization.

Create scalable pipelines that produce interaction data from teleoperation, autonomous rollouts, simulation and mixed human-and-agent methods.

Design rigorous benchmarks for manipulation, planning, robustness, safety and transfer across embodiments, and investigate where sim-to-real transfer fails.

Recruit and mentor researchers and engineers, collaborate with external labs, and publish influential work.

Qualifications

Significant experience in robotics, embodied AI, robot learning or autonomous systems.

Deep knowledge of robot learning, multimodal models, imitation and reinforcement learning, world models, planning and control.

Strong grasp of sensing, calibration, contact, actuation, latency, uncertainty and hardware constraints.

Experimental rigor, from hypotheses and controlled experiments to meaningful baselines and evidence-backed conclusions.

Sound judgment on evaluation, including distribution shift, benchmark design and failure modes of physical AI tests.

Experience setting research direction, mentoring technical talent and leading ambitious research programs under uncertainty.

Job Description

This is a staff leadership role at micro1, where the VP of Robotics Research sets the research direction for Physical AI: systems able to understand, reason about and act in the real world.

Robotics expertise matters to physical AI because sensing, contact, latency and hardware limits decide whether a model's behavior holds up outside simulation. The position is full-time and remote.

Key Responsibilities

Define the Physical AI research agenda by choosing the highest-impact open problems in robotics and embodied intelligence.

Build agents that link foundation models to robots, instruments and other physical systems.

Lead research on generalist robot learning, including VLA models, imitation learning, reinforcement learning, world models and multimodal policies.

Study which demonstrations, trajectories, sensor streams and supervision signals most improve learning and generalization.

Create scalable pipelines that produce interaction data from teleoperation, autonomous rollouts, simulation and mixed human-and-agent methods.

Design rigorous benchmarks for manipulation, planning, robustness, safety and transfer across embodiments, and investigate where sim-to-real transfer fails.

Recruit and mentor researchers and engineers, collaborate with external labs, and publish influential work.

Qualifications

Significant experience in robotics, embodied AI, robot learning or autonomous systems.

Deep knowledge of robot learning, multimodal models, imitation and reinforcement learning, world models, planning and control.

Strong grasp of sensing, calibration, contact, actuation, latency, uncertainty and hardware constraints.

Experimental rigor, from hypotheses and controlled experiments to meaningful baselines and evidence-backed conclusions.

Sound judgment on evaluation, including distribution shift, benchmark design and failure modes of physical AI tests.

Experience setting research direction, mentoring technical talent and leading ambitious research programs under uncertainty.

Robotics Research Leader for Physical AI (Trains AI Systems)

About micro1

Job Description

Key Responsibilities

Qualifications

Skills

Robotics Research Leader for Physical AI (Trains AI Systems)

About micro1

Job Description

Key Responsibilities

Qualifications

Skills