Machine Learning Engineer Recruitment

Machine learning engineers, often shortened to ML engineers, take a model out of a notebook and keep it working once real traffic arrives. Consortia recruits them for data, AI and platform businesses on permanent and contract terms across the UK, Europe, the US and the UAE.

The role sits between data science and engineering, and companies draw that line in different places. Some machine learning engineers work close to research, training and evaluating models. Others work closer to infrastructure, building the pipelines, serving and monitoring that keep predictions accurate over time. MLOps engineers have become a specialism of their own within that second group. Where the work builds on ready-made AI models, the role is more often advertised as an AI engineer.

How we recruit machine learning engineers

Before we search, we agree which end of that range you need. Some roles blend data engineering and data science in a flat structure. Others sit closer to serving and infrastructure. Once the balance is agreed with the hiring manager, we ask candidates what they have trained, deployed and maintained, and what happened when a model met real users.

Many machine learning engineer roles sit inside small, early-stage teams that plan to grow quickly. We are clear with candidates about what that brings: a fast pace, wide ownership and, in some cases, remote work across time zones.

Many machine learning processes open with a technical assessment before any interview. We make sure candidates know when it is due and how long it should take, and we keep the client updated when holidays or notice periods cause a delay.

Some clients, particularly at early stage, trade base salary against equity. We take candidates' expectations for both. When a candidate decides a start-up's pace is not right for them, we tell the client plainly.