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Operating rooms are visually complex, dynamic environments with strict reliability and privacy requirements. This role sits at the intersection of raw camera data, computer vision models, and clinical workflows — turning challenging visual input into robust, deployable perception that works in real surgical settings.

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Working at Lynqo

We build a software platform that is used in real hospital environments, with real constraints and real users. That means clarity, reliability and good communication matter just as much as technical elegance.

We value ownership, curiosity and openness. People at Lynqo take responsibility for their work from idea to deployment, invest time in understanding the context they’re building for, and aren’t afraid to ask questions or challenge assumptions.

We’re a small team with short feedback loops. You’ll work closely with both technical and non-technical colleagues, and your input will shape how the product evolves.

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Your role in a nutshell

• Full-time role, open to discuss a part-time arrangement
• Close collaboration with our CTO, AI engineer and hospital partners
• High ownership over core computer vision components, from model design to deployment
• Direct impact on real hospital workflows
• Flexible working setup (Antwerp office + remote) </aside>

Your mission ahead

Scene understanding & tracking

• Design and implement detection and tracking models for critical objects and regions in the OR
• Build and maintain pipelines that track scene elements across time
• Handle occlusions, partial visibility and dynamic scenes typical of surgical environments
• Implement privacy-aware vision components as part of the perception pipeline

Edge deployment & robustness

• Optimise and deploy vision components on edge devices
• Balance accuracy, latency and robustness under real-world constraints
• Monitor and improve runtime performance and system stability

Clinical validation & integration

• Test and iterate directly in pilot hospitals
• Analyse failure modes and improve system behaviour based on actual usage