Founding Senior Research Scientist

Position

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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 laboratory's research direction with the company's broader objectives.
This is a founding research position that combines scientific leadership with direct involvement in research. It is suited to someone who wants to investigate open scientific questions while helping shape the priorities, collaborations and research practices of a new laboratory.

Profile

You hold a PhD in machine learning, robotics, applied mathematics, physics, mechanics or a related field, followed by several years of research experience in academia or industry.
You have developed deep expertise in at least one area relevant to Physical AI. This may include world models and model-based planning, reinforcement learning, training or post-training large models, robot learning and control, physical or differentiable simulation, or self-supervised learning from video and 3D data. Your expertise enables you to assess existing approaches critically, identify meaningful research questions and develop original scientific contributions.
You have a strong research track record, demonstrated through publications in leading scientific conferences or journals relevant to your field. Your research contributions should demonstrate scientific depth, originality and relevance. Publication records should be considered in the context of yo