(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.