Founding Research Scientist at stealth physical AI startup
Jack & Jill, San Francisco, CA, United States
Job Title
Founding Research Scientist
Company Description
Stealth physical AI startup
Job Description
You will lead the research function at a venture-backed startup solving the data bottleneck in robotics. By training end-to-end manipulation policies and designing evaluation frameworks, you will bridge the gap between high-quality real-world interaction data and model performance. This hands‑on role offers significant equity and the chance to build a research stack from zero.
Location
San Francisco, USA
Why this role is remarkable
Rare opportunity to join as a founding member with minority co‑founder level equity and full ownership of the research agenda.
Work at the critical intersection of robotics and data infrastructure, solving the primary bottleneck preventing the scaling of physical AI.
Collaborate directly with a technical founding team in a high‑autonomy environment where you set the research direction and validation strategy.
What you will do
Train and evaluate real‑world robot manipulation policies using imitation learning, diffusion policies, or vision‑language‑action models.
Build the end‑to‑end data‑to‑policy validation pipeline to ensure high‑quality tactile and interaction data translates to performance.
Work alongside hardware and software teams to define the research roadmap and iterate on differentiated data collection methods.
The ideal candidate
Proven track record training manipulation policies on physical hardware, focusing on dexterous manipulation or tactile sensing.
PhD from a top‑tier robotics laboratory or extensive research experience at a leading organization specializing in physical foundation models.
Strong desire to work in an early‑stage, hands‑on individual contributor role with the ambition to build a research function from scratch.
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Founding Research Scientist
Company Description
Stealth physical AI startup
Job Description
You will lead the research function at a venture-backed startup solving the data bottleneck in robotics. By training end-to-end manipulation policies and designing evaluation frameworks, you will bridge the gap between high-quality real-world interaction data and model performance. This hands‑on role offers significant equity and the chance to build a research stack from zero.
Location
San Francisco, USA
Why this role is remarkable
Rare opportunity to join as a founding member with minority co‑founder level equity and full ownership of the research agenda.
Work at the critical intersection of robotics and data infrastructure, solving the primary bottleneck preventing the scaling of physical AI.
Collaborate directly with a technical founding team in a high‑autonomy environment where you set the research direction and validation strategy.
What you will do
Train and evaluate real‑world robot manipulation policies using imitation learning, diffusion policies, or vision‑language‑action models.
Build the end‑to‑end data‑to‑policy validation pipeline to ensure high‑quality tactile and interaction data translates to performance.
Work alongside hardware and software teams to define the research roadmap and iterate on differentiated data collection methods.
The ideal candidate
Proven track record training manipulation policies on physical hardware, focusing on dexterous manipulation or tactile sensing.
PhD from a top‑tier robotics laboratory or extensive research experience at a leading organization specializing in physical foundation models.
Strong desire to work in an early‑stage, hands‑on individual contributor role with the ambition to build a research function from scratch.
#J-18808-Ljbffr