How to hire machine learning and AI engineers at a startup

Updated September 2026

To hire machine learning and AI engineers at a startup, first decide whether you need someone to build products on top of existing models, to train and evaluate your own models, or to run the data and infrastructure underneath. Then source from people who have shipped ML to real users, and assess them on a scoped problem that resembles your data and constraints rather than on textbook theory.

Define which kind of AI engineer you need

"AI engineer" means different things to different candidates. Getting this wrong is the most common reason these searches stall. Most startups need one of three profiles:

• Applied AI or product engineer: builds features using existing models and APIs, with strong software engineering, prompt and retrieval design, evaluation and cost control.
• Machine learning engineer: trains, fine-tunes and evaluates models on your data, and takes them to production with monitoring.
• ML infrastructure or data engineer: builds pipelines, feature stores, training and serving infrastructure so others can move quickly.

Research scientists are a fourth profile, useful when your advantage depends on new methods. Few early startups need one before they have product traction. If your product depends on AI but you have no engineers yet, start with our guide on hiring a CTO or a founding engineer first.

When to hire

Hire dedicated ML talent when AI is core to what customers pay for, and when generalist engineers are spending most of their time on model quality, evaluation or data issues. If AI is one feature among many, a strong software engineer with applied AI experience may cover it for a while.

Check your data before the search. A machine learning engineer with no usable data, labels or evaluation set will spend their first months on data work, which is fine if you tell them upfront.

Where to find ML and AI engineers

• Engineers who have shipped AI features at startups, visible through product launches, write-ups and conference talks.
• Contributors to open source models, evaluation tools and ML libraries.
• PhD graduates and postdocs who want to apply their work, for model-heavy roles.
• Software engineers who have moved into applied AI and can show working projects.
• Referrals from your technical advisers and investors.

Competition for experienced ML engineers is strong. Candidates respond to specifics: the problem, the data you have, the compute available and who they will work with.

How to assess ML and AI candidates

• Project deep dive: ask them to walk through a system they shipped. Probe how they measured quality, what failed in production and how they handled cost and latency.
• Scoped practical exercise: a small problem shaped like yours, for example improving retrieval on a sample dataset or designing an evaluation for a feature. Keep it short and pay for it if it is substantial.
• System design: discuss how they would take a model from prototype to monitored production at your scale.
• Software engineering check: review code quality and testing habits, because much of the work is engineering.
• Communication: can they explain model trade-offs to a founder or customer without jargon?

Be careful with interviews that reward memorised theory. Many excellent applied engineers learned by building.

Closing and common mistakes

ML candidates weigh the problem, the data and the team heavily. Share what you can about both early, and be clear about compute budgets and who decides on model and vendor choices. Move quickly, because strong candidates tend to have several conversations running.

• Writing one job description that mixes research, applied and infrastructure work.
• Hiring a researcher when you need someone to ship features.
• Starting a search without data or evaluation plans.
• Testing only theory and not software engineering.

How Funded.club helps with AI hiring

Funded.club is a fixed-fee recruiting partner for funded startups across North America, Europe and APAC. A dedicated recruiter works with you to define the profile, headhunts candidates and screens them before you spend interview time, with first screened candidates within 7 days.

The fee is fixed and agreed upfront, which matters for roles with higher salaries. For an ML engineer at $160,000, a 20 to 25% contingency fee would be $32,000 to $40,000. Funded.club charges $15,900 to $21,900 for roles above $150,000, and $11,500 for roles up to $150,000. Funded.club's customers include Cobalt Robotics and Supernova.io. See pricing.

Frequently asked questions

What is the difference between an AI engineer and a machine learning engineer?

An AI engineer usually builds product features on top of existing models, focusing on integration, retrieval, evaluation and cost. A machine learning engineer typically trains, fine-tunes and deploys models on the company's own data. Titles vary, so define the work in the job description.

Does a startup need a PhD to build AI products?

Usually not. Most startups building on existing models need strong applied engineers. A PhD is more relevant when your advantage depends on new modelling methods.

How should I test an ML engineer?

Use a short practical exercise shaped like your real problem, plus a deep dive into a system they shipped. Include a software engineering check, because production ML is mostly engineering.

How long does it take to hire an ML engineer?

It depends on the profile and market. Funded.club delivers first screened candidates within 7 days and averages 33 days from kick-off to hire.

Worth a brief chat about your next hire? Book a free call.