Director of Robotics Research
Listing checked July 13, 2026 · pay as published by Micro1
Overview
As the Director of Robotics Research, you'll lead a team focused on building the data infrastructure that powers robot learning. You'll define how robotics data is collected, structured, and evaluated, creating a Robotics Data Engine to enable more capable systems. This role sits at the intersection of robotics, perception, and large-scale data, requiring both technical vision and hands-on research. You'll collaborate with AI researchers and engineers while mentoring a team of exceptional researchers.
What You'll Do10
- 1Shape and drive the research roadmap for robotics data, embodied AI, and robot learning.
- 2Spearhead research on robotics datasets, multimodal representations, perception, manipulation, and data-centric learning.
- 3Recruit, mentor, and scale a high-performing team of Robotics Researchers, promoting scientific rigor and rapid experimentation.
- 4Design innovative methods for collecting robotics data via teleoperation, human demonstrations, simulation, autonomous exploration, and real-world trials.
- 5Build scalable data pipelines that define collection workflows, task taxonomies, annotation schemas, quality control, evaluation protocols, and delivery formats.
- 6Translate research goals and client needs into detailed pipeline specs, including data requirements, collection strategies, labeling standards, and success metrics.
- 7Collaborate across research, engineering, product, and operations to operationalize pipeline designs and deliver production-ready robotics data solutions.
- 8Create scalable benchmarks and evaluation frameworks to measure and improve robot learning performance.
- 9Stay ahead of the field by reviewing emerging literature, datasets, and techniques from top academic labs and industry.
- 10Champion technical excellence through clear documentation, knowledge sharing, and cross-functional communication.
Requirements8
- 1Substantial background in robotics research, embodied AI, robot learning, or robotics software engineering.
- 2Proven experience leading or mentoring high-performing technical or research teams.
- 3Deep understanding of robotics data pipelines, multimodal datasets, and data infrastructure for robot learning.
- 4Strong expertise in either robot perception, manipulation, control, imitation learning, reinforcement learning, or foundation models for robotics.
- 5Experience designing and evaluating large-scale robotics datasets and collection strategies.
- 6Ability to bridge research and engineering, translating cutting-edge ideas into scalable systems.
- 7Excellent written and verbal communication skills with a history of cross-disciplinary collaboration.
- 8A genuine passion for advancing embodied intelligence through innovative data-driven research.
Who Should Apply
We're looking for a seasoned robotics researcher who has both deep technical knowledge and a proven track record of leading teams. Ideally, you've designed large-scale data pipelines for robot learning, have published at top venues like CoRL, RSS, or NeurIPS, and are comfortable with simulation environments such as ROS2, Isaac Sim, or MuJoCo. You should be equally comfortable diving into research problems and operationalizing solutions, all while mentoring the next generation of researchers.
Salary Insight
The base salary range for this full-time remote role is $250,000 – $350,000, plus eligibility for equity compensation and performance-based bonuses. micro1 also offers comprehensive benefits including up to 100% health-insurance premium reimbursement, paid time off, and a 401(K) plan with company match.
Pay and demand for Machine Learning & AI roles
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$75/hour
This role
$100–$120/hr
Most Machine Learning & AI roles pay $55–$101 per hour. This role's pay sits above that range.
Based on 471 similar roles that publish pay · 93 publish only a top rate; those count at the rate they gave
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Compensation
$100–120/hr
Required Skills
Application Tip
In your application, include specific examples of robotics data pipelines you've built from scratch—mention the scale, the data modalities involved (e.g., vision, tactile, force), and the impact on robot learning performance. Links to relevant publications or open-source projects (like a contribution to Isaac Sim or Habitat) will set you apart.
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