Machine learning engineer in computer vision

The video understanding pipeline—from multi-object tracking and re-identification to action recognition—is yours to own and scale. The technical challenge lies in balancing high-throughput, low-latency GPU inference with cutting-edge detection accuracy across massive streams of sports footage. You will establish rigorous benchmark suites and decide which vision foundation models make it into production.

• Own the video understanding pipeline, including object detection, multi-object tracking, player re-identification, jersey-number recognition and on-screen text recognition (OCR).
• Own event and action recognition, including temporal localisation of key moments, shot and scene-boundary detection, replay detection and camera-view classification.
• Optimise GPU inference for throughput and cost through batching, precision tuning and compilation.
• Monitor production quality and latency, and investigate failures and regressions.
• Build and maintain labelled sports benchmarks, split by sport, league and content type.
• Define and track key metrics, including mAP, F1, precision, recall, IoU, latency and GPU cost.
• Run systematic bake-offs comparing new detection, tracking, segmentation, re-identification and video foundation models. Reproduce published results on our data.
• Write decision memos with clear recommendations, trade-offs and licensing considerations.
• Own the regression suite and shadow or A/B testing pipelines, ensuring every model or configuration change is validated before deployment.
• Track new papers, model releases and leaderboards, and identify which developments warrant further evaluation.

• 3–5+ years of experience shipping production ML systems, including 2+ years in computer vision, ideally working with video.
• Strong Python and PyTorch skills, including hands-on experience profiling and optimising GPU inference using TensorRT and ONNX Runtime.
• Hands-on experience in at least two of the following areas: object detection and segmentation (YOLO, DETR-family, SAM), OCR and scene-text recognition, or video action recognition and temporal localisation.
• Experience building test sets, calculating evaluation metrics such as mAP, F1 and temporal IoU, and running rigorous model comparisons.
• Ability to critically evaluate new research papers and model cards, and reproduce published results.
• Production deployment experience with Docker, queue-driven services and cloud GPU infrastructure.
• Experience with video processing at scale, including FFmpeg, video decoding pipelines and frame sampling.
• Strong written communication skills, with the ability to document technical trade-offs and make clear recommendations.
• Будет плюсом: Experience with Azure (Service Bus, Blob Storage) and message-driven architectures; Experience with vision-language models and multimodal understanding across video, audio and text; Experience fine-tuning or adapting vision models to specific domains, particularly sports video; Experience with self-supervised learning or synthetic data generation to reduce labelling costs; Familiarity with annotation tools such as CVAT and Label Studio, and experience working with labelling teams; Experience with shadow deployments and A/B testing; Familiarity with commercial model licensing

• Direct, unconstrained ownership of the full ML lifecycle—from paper replication and benchmark creation to live GPU optimization.
• Production impact at scale, where your model optimization directly lowers real-world infrastructure costs and improves latency on high-volume video data.
• High-rigor, evidence-driven engineering environment that prioritizes A/B testing, shadow paths, and statistical validation over hype.