Founding Senior Research Scientist – Physical AI

Company

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Adoc Talent Management is supporting the recruitment of a Founding Senior Research Scientist – Physical AI for a San Francisco-based company, part of Y Combinator’s 2026 cohort, whose work focuses on collecting human and robotic data to train robots.

The company specializes in collecting first-person, real-world data, at a scale and speed that research laboratories cannot readily achieve independently. To ensure traceability and quality, each contributor’s identity is verified through KYC procedures, and every contribution undergoes several quality-control checks before reaching clients.

Looking ahead, the company aims to play a leading role in deploying robots at scale and making the data needed to advance Physical AI more accessible.

To support this ambition, the company is establishing Hub Labs, a research laboratory in Toulouse, France. With access to a large and varied body of data collected directly from contributors, the laboratory will conduct research to support the future deployment of the company’s robot fleet.

The team develops the infrastructure needed to turn research ideas into results quickly and at scale, transforming those results into data that can be used by teams training future AI models.

Position

As Founding Senior Research Scientist – Physical AI, you will contribute to defining and developing the research program of a new laboratory. Working closely with the laboratory's leadership, you will combine scientific independence with hands‑on research, helping determine which questions to investigate and how to build a coherent research direction over time.

A central area of interest is world models and the role of real‑world data in Physical AI. Two important questions are what data AI systems need to learn about the physical world, and what data enables them to adapt as conditions change after deployment. These questions provide a starting point for the research program, while leaving room for you to identify promising approaches and propose new directions based on your expertise.

Your first priorities will include assessing the state of the art, identifying relevant research opportunities and contributing to the laboratory's initial research roadmap. This will involve balancing immediate company needs with longer‑term scientific ambitions, preparing an initial research survey, identifying opportunities for a research paper or workshop contribution, and helping develop a longer‑term research vision over a five-to‑ten‑year horizon.

You will then investigate selected research questions through original scientific work. This includes formulating hypotheses, developing and testing approaches, designing experiments, analyzing results and remaining directly involved in coding and experimentation. Relevant research areas may include world models and model‑based planning, reinforcement learning, large‑model training and post‑training, robot learning and control, physical simulation, and self‑supervised learning from video and 3D data. The precise direction will depend on your expertise and the research priorities established with the team.

Scientific publications will be an important part of the position. You will contribute to research intended for leading conferences and journals in machine learning, computer vision and robotics, and engage with workshops and research discussions involving leading academic and industrial teams. Establishing the laboratory's scientific credibility will be a key objective, supported by rigorous research, relevant publications and meaningful engagement with the wider research community.

The role also involves developing research collaborations. You will work with external AI and robotics teams using the company's data, exchange ideas with academic and industrial researchers, and contribute to collaborative research initiatives. Where appropriate, research outputs may include open benchmarks, datasets and other openly available contributions. The intention is to support scientific publications and open research, with the boundaries around confidential work established in advance.

The research environment includes large‑scale, real‑world recordings, a teleoperation laboratory in Toulouse with robotic cells, and computing resources for machine learning research. These resources provide opportunities to investigate the relationship between data collection, model learning and the development of physical AI systems.

You will also contribute to the development of the research team by supervising and mentoring junior researchers, PhD students and interns, where applicable. In addition, you will communicate relevant scientific developments and publication trends to company leadership, helping connect the labor