You will build the foundation model layer that makes manual labeling optional for surgical video. By training self-supervised video encoders and spatiotemporal transformers on raw 2-6 hour surgical footage, you will enable machines to understand tool-tissue interactions and procedure progression, directly shaping the future of autonomous surgical robotics and AI-driven operative reporting.
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Foundation Modelling Engineer at Uncovr
Join an EF-backed Paris startup building the foundation model layer for the next generation of surgical robotics. As a Foundation Modelling Engineer, you will tackle the immense challenge of teaching machines to understand complex 2-6 hour surgical procedures from raw video alone, without relying on brittle, manually-labeled steps. Working alongside a world-class team from ETH Zurich and Oxford Dynamics, you'll build self-supervised video encoders and spatiotemporal transformers that transform raw footage into structured intelligence. This is a rare opportunity to join a high-growth AI team of fewer than ten people where your technical decisions will directly shape the future of robotic autonomy.
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Location
Paris, France
Compensation
Not Disclosed
Company
Uncovr
Role overview
Healthtech
Medtech
AI10 employeesSeed (VC-backed)
Foundation Model for Surgery, building for surgical efficiency today and Surgical robotic autonomy tomorrow.
What you will do
- Train self-supervised video encoders using masked video modeling and temporal contrastive learning on laparoscopic and robotic video datasets.
- Build spatiotemporal transformers to track tool-tissue interactions and construct scene graphs that describe surgical actions independent of tool brands.
- Develop production-grade training infrastructure and data pipelines to turn massive hospital video streams into scalable, high-performance training workflows.
Who this is a fit for
- 5-8 years of hands-on experience training large-scale vision or video models, with deep expertise in representation learning and self-supervised training.
- Proficiency in optimizing distributed PyTorch training for long-context temporal modeling and handling multi-hour video sequences.
- A proven builder mindset with the ability to bridge high-level research and production-grade engineering, ideally supported by publications at CVPR, NeurIPS, or ICLR.
Why this role is remarkable
- Work with a world-class team from ETH Zurich, ESA, and Oxford Dynamics, advised by Prof. Dan Stoyanov, a global leader in surgical computer vision.
- Tackle one of AI's hardest frontiers: training large-scale foundation models on complex, long-context video data where labels are traditionally scarce and brittle.
- Join an early-stage startup as one of the first ten employees, gaining meaningful equity and the autonomy to design technical architectures from scratch.
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