Machine learning engineer

Level

Degree level

Length

2 yrs

typical

Sector

Digital

Pay

Paid

you earn while training

In a nutshell

The ML Engineer gathers data from different sources to design, build, deploy and validate machine learning and or artificial intelligence solutions.

Define ML problem framing and select appropriate modelling approaches with stakeholders

Collect, clean, and engineer features from large and complex datasets

Train, evaluate, and iterate on machine learning models to optimise performance metrics

Deploy models to production environments using container and cloud infrastructure

Monitor model performance and detect data drift or degradation in live systems

Document experiments, model cards, and technical findings for cross-team review

Review and improve existing ML code for accuracy, efficiency, and maintainability

Mathematical foundations of machine learning including linear algebra, probability, and optimisation

Supervised, unsupervised, and reinforcement learning algorithms and their appropriate applications

Deep learning frameworks including neural network design and training

Data engineering pipelines for ingesting, transforming, and validating large datasets

MLOps practices for deploying, monitoring, and maintaining models in production

Responsible AI principles including fairness, explainability, and bias mitigation

Software engineering best practices including version control, testing, and code review

Ready to look for a place?

See live Machine learning engineer apprenticeship vacancies on the official government service.

A Careermash concept · apprenticeship standard from our careers engine · vacancies via gov.uk.