Computer Vision Engineer (Sports Analytics)
Morson Edge
- London, England, United Kingdom
- London, England, United Kingdom
About
In this role, you will design, build and deploy computer vision systems that extract reliable information from video and sensor streams to create production‑grade sports analytics features. This includes model development, evaluation, optimization for real‑time or near‑real‑time performance and robust deployment into live products.
Key Responsibilities
Build CV/ML pipelines for sports analytics tasks such as detection, segmentation, pose estimation, tracking, action recognition, ball/player tracking, or 3D reconstruction
Develop and maintain data pipelines: collection, labeling strategies, quality checks, dataset versioning, and experiment tracking
Train, tune, and evaluate models with strong statistical rigor and clear metrics (accuracy, latency, stability, drift)
Optimize models for deployment (quantization, pruning, TensorRT/ONNX, batching/streaming, GPU utilization)
Collaborate with product, design, and engineering teams to integrate models into user‑facing features and services
Monitor models in production: performance dashboards, alerts, retraining triggers, and incident response
Contribute to technical direction, including architectural choices, tooling, standards, and best practices
Produce clear technical documentation and communicate trade‑offs to non‑specialists
Demonstrable sports analytics experience (professional, academic, personal projects, or hobbyist) such as match analysis, player tracking, event tagging, tactical analysis, or building tools using sports data/video
Strong practical experience in computer vision and deep learning, with evidence of shipped systems or robust prototypes
Excellent Python skills, plus solid software engineering fundamentals (testing, CI/CD, code review)
Experience with PyTorch (preferred) or TensorFlow; familiarity with OpenCV and modern CV tooling
Strong understanding of CV fundamentals (geometry, camera models, multi‑view, filtering) relevant to the role
Experience deploying ML to production (APIs/services, edge or cloud inference, containerization)
Comfortable working with GPUs and performance profiling/optimization
Nice to Have
Football or basketball‑specific analytics experience or other elite‑sport performance analysis
Experience with pose models (2D/3D), IMU fusion or biomechanics‑related estimation problems
C++ for performance‑critical components
Experience with AWS/Azure/GCP and MLOps tooling (MLflow/W&B, feature stores, model registries)
Knowledge of real‑time systems, streaming pipelines and low‑latency inference
If you are passionate about sports, computer vision, and building products that make an impact, we want to hear from you!
Please note, this role unfortunately does not offer sponsorship.
Seniority level:
Mid‑Senior level
Employment type:
Full‑time
Job function:
Information Technology and Engineering
Industries:
Spectator Sports, Sports Teams and Clubs, Technology, Information and Media
#J-18808-Ljbffr
Languages
- English
Notice for Users
This job comes from a TieTalent partner platform. Click "Apply Now" to submit your application directly on their site.