(INTERNAL ONLY) SENIOR ENGINEER - COMPUTER VISION / MACHINE LEARNING Location: UK (London) preferred, Hungary considered Contract: Permanent full-time Level: 4 ABOUT THE ROLE You'll own the CV and physical-modelling layer within DemTech's tracking pipeline - working on top of ML-provided detection models to produce trajectory and positional outputs (e.g. trajectory estimation, motion reconstruction, 2D-to-3D reconstruction problems). This role exists because tracking data is only as useful as the reconstruction layer that sits between detection and the outputs people actually rely on - dashboards, officiating decisions, performance insight. You'll own that layer within a small, fast-moving sports technology product team, working with real-world data. You'll also be expected to work within DemTech's AI-first ways of working - using AI-delegated and AI-augmented development practices as a normal part of how you build, not as a separate initiative layered on top. Key Responsibilities * Own the day-to-day delivery of the 2D-to-3D reconstruction pipeline - converting raw detections into positional and trajectory outputs using physics-based modelling (trajectory estimation, motion reconstruction, projectile physics), operating with autonomy within agreed direction. * Work directly with the ML discipline team on model performance - proposing and prototyping improvements where applied CV work surfaces opportunities, rather than only consuming their output. Strong performers here are expected to shape R&D-adjacent proposals, not just execute them. * Hold a genuine voice in technical decisions on algorithm design and data pipeline structure - contribute to architectural milestones and offer insight on peers' work, including alternative solutions and design tradeoffs. * Own the accuracy and reliability of tracking outputs across variable, real-world deployment conditions. * Support optimisation and deployment of models onto embedded, resource-constrained hardware, using deployment techniques such as TensorRT, ONNX, quantisation, pruning, and bottleneck profiling. * Use AI-delegated and AI-augmented development practices as a standard part of the role. Key Attributes & Skills * Strong applied computer vision experience, with solid grounding in mathematical and physical modelling - trajectory estimation, motion reconstruction, projectile physics, or comparable 2D-to-3D reconstruction problems. * C++ required; Python experience is a plus for prototyping and tooling. * Working knowledge of ML techniques, with genuine interest in contributing to model improvement conversations and proposing R&D-adjacent ideas - core training and validation sit elsewhere. * Practical experience with model deployment and optimisation tooling (e.g. TensorRT, ONNX, quantisation, pruning, bottleneck profiling) for embedded or resource-constrained environments. * Experience with camera-based data sources, tracking pipelines, or spatial/temporal data. * Comfortable with ambiguity - this is a build-phase product with evolving scope. * Strong communication skills - able to work with data platform, backend, and frontend engineers on data contracts and outputs, and to explain complex problems and solutions clearly to others. What This Role Is Not * Not a primary ML research role - core model training and validation sit with the ML discipline team or associated ML engineers, though close collaboration and proposing improvements is expected. * Not a data engineering role - a separate role owns storage, transformation, and API exposure of the outputs produced here. * Not a people-management role by default - this is an individual contributor position with genuine technical ownership of the CV/reconstruction domain, day-to-day and under agreed direction rather than final sign-off authority.