The team :

Our Video Analytics team builds a production, multi-tenant computer-vision platform for retail and QSR. It runs inference on the edge, inside the store, turning existing CCTV into live footfall, demographics, dwell, occupancy, queue, service-time and compliance intelligence with real-time alerts and dashboards on top. It's deployed across live brand locations today and is scaling toward a large fleet of edge devices across hundreds of stores.

This is a dedicated, cross-functional group computer vision, backend, frontend and platform engineers working together on one product. We're hiring a senior computer vision engineer to take ownership of the on-device intelligence that makes the whole thing work.

What you'll own :

- The core on-device CV stack detection, tracking and per-feature analytics engineered to run reliably within the compute and memory budget of edge hardware, at production quality across a growing device fleet.

- Model performance on the edge : taking models built for the GPU and making them fast and accurate on constrained devices NCNN/ONNX export, int8 quantisation, resolution/cadence tuning, and a shared-inference design that lets one box serve many features.

- The analytics feature set : footfall & demographics, dwell and zone/table occupancy, queue and service-time, PPE/SOP compliance, and cross-camera person re-identification.

- Accuracy in the real world building the evaluation harnesses and ground-truth workflows that hold every feature to a measurable bar across varied camera angles, lighting and occlusion, rather than trusting benchmark FPS.

- The path from model to fleet partnering with the platform team on deployment, OTA model updates and monitoring so accuracy holds across every store, not just the lab.

- A voice in the CV roadmap and engineering standards which models we invest in, how we evaluate, how we ship and helping level up the vision engineers around you.

What you bring :

- 3+ years of computer-vision / deep-learning engineering with systems that reached production.

- Deep hands-on with object detection (YOLO family) and multi-object tracking (ByteTrack, DeepSORT or similar) on real camera / RTSP video.

- Real experience optimising and deploying models for inference ONNX, quantisation (int8), and at least one edge/runtime stack (NCNN, TensorRT, OpenVINO or TFLite) on devices like Raspberry Pi or Jetson.

- Strong Python and OpenCV, and a genuine feel for the accuracy - latency - cost trade-off with the rigour to measure it.

- Ownership instinct : you can take a fuzzy problem to a shipped, monitored feature and hold yourself to production standards.