Skip to main content
Careermash
CAREERMASH (EN)FROM COMPUTER SCIENCE →

Machine Learning Engineer

Machine learning engineers build computer systems that learn from data and get better over time. They write code and test models that help businesses make predictions, spot patterns, and automate decisions - from spotting fraud to recommending films.

Machine Learning Engineer

72

Computer Science

AI IMPACT
AI EXPOSURE TODAY72
20-YEAR SCENARIO
NOT A FORECAST90%
THE DOOR · ENTRY DIFFICULTY TODAYC
STARTING PAY£30,000 - £40,000
NO MOATNo physical, legal or personal barrier protects this work from software.

THE DOOR: ENTRY DIFFICULTY TODAY, graded A (easy) to E (extremely hard). A Careermash rule-of-thumb index built from our AI exposure index and the job's moat class. It is not a forecast and not a count of job openings.

AI EXPOSURE TODAY is a Careermash index from 0 to 100 of how exposed this job's everyday tasks are to AI today. We estimate it by matching published research on how people use AI to this job, usually by keyword or subject area, and we lower it where the work is protected (for example by physical, personal or legal requirements). It is not a direct measurement of how much people in this job use AI.

This work is being absorbed by AI
AI exposure is high for this work and nothing structurally requires a human: no licence, no physical task, no relationship at the core. Enter as the person directing the tools, not competing with them.

The role

As a machine learning engineer, you write computer code that teaches machines to learn from data. You work with vast amounts of information - numbers, patterns, customer behaviour - and build systems that spot trends and make predictions. These systems help hospitals diagnose diseases faster, banks catch fraud, shops recommend products customers want, and lots of other useful things.

Your day involves writing code to build and test these learning systems, working closely with data specialists and other engineers. You will feed data into your system, run tests to check it works, tweak it when results aren't good enough, and then launch it into the real world where it keeps working. You need to stay curious about new techniques, solve tricky problems when things go wrong, and keep learning because this field changes fast. It is technical work but incredibly rewarding when your system starts making real predictions and helping real people.

Daily responsibilities

  • Design and implement machine learning models tailored to specific business needs.
  • Collaborate with data scientists and software engineers to integrate machine learning algorithms into applications.
  • Conduct experiments to test and tune algorithms for optimal performance.
  • Analyze large datasets to extract meaningful patterns and insights.
  • Deploy machine learning models into production and monitor their performance.
  • Stay updated with the latest advancements in machine learning technologies and methodologies.
  • Document processes, code, and model performance for future reference and team collaboration.

Does a degree help here?

A UK degree, particularly in Mathematical Sciences or Computer Science, provides a robust foundation in analytical thinking and problem-solving. UK universities are renowned for their rigorous curricula and strong industry connections, giving graduates a competitive edge in the job market.