Principle Machine Learning Engineer
Contract
Pay DOE
Hybrid 2x a week on site
Most sports data is reactive - stats after the moment’s gone.
This role is about building systems that generate real-time insights during live sport.
Why This Role?
- 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
What You’ll Bring
- 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
Ideal Profiles
- 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)
No need for a perfect CV if you’ve built ML systems that run in production, let’s talk.
I can get you directly in front of the team quickly.