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VP of Robotics Research

$6000K
per year
Full-time Remote Last verified 21 Jul 2026
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About the Role

Job Title: 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. Our research focuses on four connected areas:

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.

You're a Good Fit If You

  • Required skills: Robotics Software, Robotics Data, Robotics 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.

Role Highlights

Pay
$1200K-$6000K/yr
Contract
Full-time
Location
Remote
Category
Research
Posted
28 days ago
Job ID
WE-7554

Pay & Payout

Engaged directly through micro1. Apply via the link below - micro1 handles onboarding and payment.

Common Questions

micro1 has a reliability rating of reliable based on the listings we track, with an onboarding time of 1-3 days. See our full micro1 review for the details behind that rating.

Applying takes you to micro1's own website, where the application is completed. We are not a recruiter or employer and do not process applications directly - we may be recorded as the referring source.

$1200K-$6000K/yr - Engaged directly through micro1. Apply via the link below - micro1 handles onboarding and payment.

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.

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