Principle Machine Learning Engineer
Postaladdress Uk
3 days ago
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
EnglishJob location
Tech stack
A/B testing
Artificial Intelligence
Computer Vision
Cloud Computing
Continuous Integration
Data Systems
Monitoring of Systems
Python
Machine Learning
TensorFlow
Data Streaming
Systems Integration
PyTorch
Generative AI
Low Latency
Real Time Data
Machine Learning Operations
Stream Processing
Job description
- Leading global streaming / sports platform
- Real ownership of ML systems at scale (millions of users)
- Solving complex real-time + low latency AI problems
The Company / Product
You'll be working on a cutting-edge platform transforming how fans experience live sport using AI to deliver personalised insights, predictions, and real-time data during live events.
What You'll Be Working On
- Leading development of ML systems for live sports insights + personalisation
- Building solutions across Computer Vision, ML, and Generative AI
- Turning live video + sports data into real-time predictions and insights
- Designing low-latency, high-scale ML systems in production
- Driving end-to-end MLOps (CI/CD, monitoring, retraining, deployment)
- Integrating ML outputs into personalisation engines
- Owning experimentation, A/B testing, and performance metrics
- Mentoring engineers and setting technical direction across teams
Tech Stack
- Python
- PyTorch / TensorFlow
- MLOps (CI/CD, model monitoring, retraining pipelines)
- Real-time / streaming systems
- Cloud-based ML infrastructure, * Principal / Staff ML Engineers in streaming, sports, or media
- ML Engineers from real-time / low-latency environments
- Engineers working on computer vision, personalisation, or live data systems
(This isn't a research role - it's production, scale, and real-world impact)
Requirements
- Strong experience building production ML systems at scale
- Experience working with real-time or streaming data
- Deep understanding of sports data (event, tracking, or video)
- Hands-on experience taking models from research * production
- Strong technical leadership and mentoring experience