How to Build an ML Team: A Complete Guide for Founders

Frequently Asked Questions

01.Who should be the first hire on an ML team?

If your records are still gathered by hand, hire or contract a data engineer first, because no model can be retrained repeatably until a pipeline delivers versioned data. If a pipeline already exists and the open question is whether a model can beat the current process, the modelling seat comes first. Rate your MLOps maturity before writing either job description.

02.Can I start with an MVP before hiring a full ML team?

Yes. A data engineer and one modelling seat, either of them contracted, can ship a first model if a named business owner signs the success metric before work starts. Hold the permanent ML engineer hire until releases need automation, which is the point where manual hand-offs between the modeller and the application team start to cost more than the seat.

03.Should an ML team be central or embedded in product teams?

Start central while you run one or two models, so the scarce pipeline and modelling seats share one pipeline and one backlog. Embed engineers in product teams once several products each need a model on their own release schedule, and keep the pipeline and its tooling with a small central owner so every embedded engineer builds on the same pipeline.

04.Does the EU AI Act require specific roles on an ML team?

It names no job titles, but two duties shape the chart. Since 27 July 2026, providers and deployers must take measures to support the AI literacy of staff and others operating AI systems on their behalf, contractors included. Providers of high-risk systems will also need a quality management system with an accountability framework for staff responsibilities, from 2 December 2027 for Annex III uses (Regulation (EU) 2024/1689, Articles 4 and 17, as amended by Regulation (EU) 2026/1744).