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# Applied Machine Learning Engineer

- **Company:** THE OPPORTUNITY HUB UK LTD
- **Location:** Cambridge, UK
- **Experience:** Experienced
- **Salary:** £49,500.0 - £77,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Python (Programming Language), Machine Learning, Data Processing, Pytorch, Machine Learning Operations, Software Version Control
- **Published:** October 23, 2026
- **Apply:** https://www.jobs.service.gov.uk/jobs/6adb0b33fc1374228e98094f/apply

## About the Role

Solid experience building and shipping machine learning models in Python, ideally with PyTorch or TensorFlow

Comfort with the full lifecycle: data wrangling, training, evaluation, deployment, monitoring

Working knowledge of MLOps practices, including version control for models and reproducible pipelines

A track record of explaining technical trade-offs to non-technical stakeholders without dumbing it down

Mid-level confidence to work independently while still asking the right questions

## Description

Ambitious Applied Machine Learning Engineer wanted to drive real impact in AI & Technology from Cambridge.

Cambridge has quietly become one of the UK's densest clusters for applied AI work, and this business sits right in the middle of it. Established but growing, this SME builds machine learning systems that solve genuine operational problems rather than chasing headlines. They need an Applied Machine Learning Engineer who can take a model from notebook to production and actually care what happens after deployment.

Here's what you'll be doing:

Design, train and deploy machine learning models that solve specific business problems, not just theoretical ones

Build and maintain data pipelines that feed models reliably, at scale, without babysitting

Work closely with product and engineering teams to translate vague requirements into working systems

Monitor model performance in production and retrain or adjust when things drift

Write clean, testable code and document your decisions so the next person understands why, not just what

Contribute to technical discussions on architecture, tooling and where AI & Technology practices at the company should head next