Computer Vision Engineer

Il y a 5 jours

Destelbergen, Vlaams Gewest, Belgique IntelliProve Temps plein

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IntelliProve builds a software platform that turns a face video into vital signs. The work ahead is to make the backend and cloud infrastructure behind that platform genuinely production-grade. Reliable enough to carry medical traffic, traceable enough to pass an audit, and ready to scale with the product. This role owns the Python/FastAPI backend and the AWS infrastructure that make that possible.
While professional experience and qualifications are key for this role, make sure to check you have the preferable soft skills before applying if required.
Working at IntelliProveAt IntelliProve we're making proactive and preventive health monitoring effortless, turning a short face video into objective vital-signs data for both consumer apps and clinical tools.
We're a 14-headed team in Ghent building two products on the same rPPG technology: a consumer wellbeing app that's live today, and a medical device heading for CE marking. Working on both in parallel keeps us close to real users and real regulators at the same time. Feedback loops are short, and decisions get made by the people doing the work. We value ownership, curiosity and clarity. People at IntelliProve take responsibility for what they ship from design through deployment, ask questions instead of guessing, and prefer clear, maintainable solutions over clever ones. You'll work side by side with engineers, ML researchers and regulatory colleagues, and your input will shape how the platform evolves.
The role at a glanceFull-time role, open to a freelance arrangementDedicated computer vision ownership of the rPPG signal-extraction pipelineClose collaboration with our CTO, ML colleagues, backend engineers, QA lead and regulatory teamDirect impact on the rPPG engine that powers IntelliProve's vital-signs measurementGhent office, up to 2 days remote per weekWhat you'd be working onrPPG signal extractionImprove how the pipeline detects and tracks the facial regions that carry the strongest blood-volume-pulse signalPush the robustness of signal extraction against the noise and variability of real-world consumer-camera dataTackle motion compensation and ROI-tracking problems that rarely have clean off-the-shelf answersModels & architecturesDesign and train deep-learning CV models in PyTorch, with a strong emphasis on temporal architectures (3D CNNs, temporal convolutions, transformer-based temporal models), since the blood-volume-pulse signal lives in the temporal dimensionFind ways to leverage the unlabelled video data captured from live users to make models more robust over timeProduction & medical validationBring models into production: real-time inference, model compression, ONNX, sensible deployment on our AWS stackImprove our MLOps so model training, evaluation and rollout scale with the productProduce the documentation, validation evidence and explainability artefacts the medical pipeline requires for CE markingWho are we looking forSeveral years of professional experience in computer vision and/or machine learning at a medior level (+3 years), or equivalent practical experience with a relevant educational background.
Strong computer vision background. Hands-on experience with classical CV (OpenCV, colour spaces, signal/frequency analysis, ROI tracking) and modern deep-learning CV (CNNs, vision transformers). Comfortable with face detection, landmarking, segmentation and trackingReal experience with video and temporal modelling. You've extracted information from frames over time using 3D CNNs, temporal convolutions, RNN/LSTM/Transformer-based temporal models, or optical flow. Bonus if you've worked on physiological signal extraction, motion compensation, or any problem where the temporal dimension carries the information rather than the spatial onePyTorch as your primary framework. Equivalent fluency in TensorFlow or JAX is acceptable if you're happy to move to PyTorchComfortable with image and video data pipelines and the practical realities of working with consumer-camera footageSolid engineering hygiene and a research mindset; comfortable working iteratively when the path to a solution isn't laid out for youNice to haveDirect experience with rPPG or physiological signal processingReal-time inference

experience:
model compression, quantisation, ONNXMLOps on a major cloud (we use AWS). Our setup is intentionally basic today and needs to scale with the productExplainable AI techniques. Useful for documenting ML components for medical certification: the clearer the explanation of how a model works, the smoother the regulatory pathA few honest self-checksYou're comfortable with research ambiguity, willing to read papers, prototype, fail and iterate when the path to a solution isn't obvious You know where rigour matters (medical paths, validation evidence) and where pragmatism wins; you're systematicwithout being slow You take operational ownership of what you ship. Models drift, edge cases surface, and you don't throw them over the wall You communicate cl

Computer Vision Engineer

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