This role is with one of Dex’s trusted partner companies. We work closely with their teams to truly understand their culture, goals, and what they’re looking for, so we can match you with the right opportunity and give you context about the role before you commit to a process.
The role
Forget incremental improvements. This company is building the surgical intelligence layer, a platform that combines computer vision, AI, and augmented reality to give surgeons and robots real-time 3D navigation. Their core differentiator: a surgical foundation model, trained on kinematics, tissue interactions, and surgeon intent captured across deployed endpoints. This isn’t academic research; it’s about shipping models that fundamentally change surgical outcomes.
This isn’t a research role. You’ll lead the AI pillar end-to-end, building a team from the ground up. Your mandate: ship production AI. That means delivering an anatomy recognition model for neurosurgery (prototype by Q2 2028, ≥85% accuracy), then evolving the foundation model from computer vision to vision-language-action for device automation. You’ll architect the data pipeline, own the federated learning stack, and define the boundary with the simulation team on synthetic data and sim-to-real transfer. Models in research are interesting; models in the operating room are valuable.
The work
- Deliver a working prototype of the anatomy recognition model for neurosurgery by Q2 2028, hitting ≥85% segmentation accuracy.
- Lead the surgical foundation model program: from computer vision and segmentation, to vision-language, to vision-language-action models for device automation.
- Architect the data pipeline for kinematics and tissue-interaction data across all deployed endpoints.
- Own the federated learning stack, deploying NVIDIA FLARE for privacy-preserving model improvement.
- Define the boundary and partner with the simulation team on Isaac Sim, synthetic data, and sim-to-real transfer.
What You Bring
- You’ve shipped production AI/ML models. Computer vision, segmentation, or vision-language experience is ideal.
- Experience with medical imaging (DICOM, segmentation networks, MONAI) or a rapid ability to ramp into it.
- You’ve worked with real-time inference constraints: sub-20ms latency, deployment to edge hardware like NVIDIA AGX.
- You own DGX/RTX-class training infrastructure and MLOps. Federated learning experience is a strong plus.
- You’ve led at least 2-3 engineers and want to grow a full AI pillar.
- Based in or around London, or able to work in person 3+ days a week.
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