AI-native engineering hire
Hire AI and machine learning engineers for your product’s next step.
Find engineers who can choose an appropriate approach, build it into your product and measure whether it works. We scope the required depth across classical ML and generative AI.
First shortlist: 5 business days after the approved brief and signed mandate. Timing confirmed for your search.
Standard recruitment
Your search at a glance
Standard: 18% of first-year salary, paid on placement. No upfront fee; 90-day replacement guarantee. We confirm the fee basis for contractor engagements in your mandate.
- Seniority
- Senior AI/ML engineer searches; the technical bar is agreed in your brief.
- Compensation guide
- Indicative planning band: €58–98K a year in senior B2B contractor compensation across CEE.
- Search markets
- CEE candidates, with location and working-hour requirements agreed for your team.
Compensation is indicative. We agree the salary band, locations and working-hour overlap before search; the placement fee is separate.
Evidence for your hiring decision
CV evidence, a role-specific technical interview and recruiter notes, with a human review before the shortlist reaches your team.
Choosing the role
Choose the responsibilities before the AI/ML title
An AI/ML engineer turns a data or model capability into a useful product behaviour. That can mean a classifier, forecasting model, recommendation system or an LLM-backed feature. The title covers several specialisms, so the hiring brief should name the problem, available data and responsibilities the engineer will own.
A generalist can be useful when the roadmap spans different approaches and needs one person to connect modelling with application delivery. They still need a defined area of depth. A single hire should not be expected to own advanced research, data infrastructure, product engineering and round-the-clock operations without support.
Specialise where the product requires it. A retrieval-heavy feature may call for a RAG engineer; language-model application delivery may call for an LLM engineer. For a classical ML role, experience with the data type, evaluation method and deployment setting can matter more than recent generative AI work. A first AI hire can be either a specialist or generalist, depending on the actual problem.
Bring to the intake: the user or business decision to improve, accessible data, an existing baseline if there is one, deployment constraints and the people available to review domain quality. We identify what the hire can reasonably own and the supporting work that needs another owner.
Across Recruo searches
Our recruitment results so far
Company-wide observations reported by Recruo’s founder on 11 September 2026. These describe our overall experience, rather than a separate cohort for this role or region.
- Pass every client assessment stage
≈9/10
Presented candidates who successfully pass the client’s full assessment process, including technical interviews.
- Candidates the client would offer to
≈4/5
Client willingness to make an offer after assessment. Offers sent, accepted and hires are separate outcomes.
- Completed probation periods passed
100%
All placed candidates whose probation has concluded have passed so far. Ongoing probation periods are excluded.
How to read these results
Pass every client assessment stage: Candidates who pass all client assessment stages, relative to candidates presented. Around 9 in 10 is an approximate company-wide observation, not a published sample of exactly ten people. Offers, acceptance and starts are separate outcomes; sample size, reporting period and treatment of ongoing assessments are not yet published.
Candidates the client would offer to: Presented candidates the client considers suitable for an offer, divided by candidates presented. Around 4 in 5 describes willingness to offer; it does not mean four offers or hires for every five candidates.
Completed probation periods passed: Placed candidates who passed probation, divided by placements whose probation period has ended. Probation lengths vary by engagement; this is not a 90-day retention measure or a guarantee of future outcomes.
Sample sizes and the reporting period are not yet published. The candidate ratios are approximate; none of these results is a forecast for your vacancy.
Example scorecard
Evidence to look for in your shortlist
Illustrative assessment criteria, not a candidate profile or a record of a completed placement. The final scorecard is agreed for your role.
Example scorecard: senior AI/ML product engineer
Role scope: Use this scorecard to assess whether a candidate has the technical depth and delivery breadth required for your product problem.
- Problem definition: connects a modelling task to the product decision and establishes a useful baseline.
- Data judgement: explains data suitability, validation design and how to check for leakage.
- Approach selection: can compare a conventional ML model, an LLM approach and a simpler non-model solution where relevant.
- Delivery: describes the integration, testing and monitoring needed to keep the feature useful after release.
- Scope awareness: makes their specialist strengths, transferable experience and support needs explicit.
How we source
How we assess breadth without losing depth
We use targeted sourcing across relevant ML, data science and software engineering backgrounds. We look for responsibility that matches the role rather than a fixed number of libraries, competition medals or public model downloads. Candidates with confidential commercial work can demonstrate the same skills through an agreed discussion or work sample.
The interview starts with a decision the candidate made: what baseline they used, how they evaluated an alternative and how the resulting system reached users. For a classical ML role, follow-ups can examine data leakage, validation design and changing input distributions. For a mixed ML and LLM role, we explore when each approach is appropriate. AI supports the interview, and a human recruiter reviews the evidence before delivery.
We document where a candidate has depth, which adjacent tasks they can own and where they would need support. Your team receives that context alongside CV evidence and recruiter notes, so its technical interview can focus on the unresolved questions instead of repeating a broad screening exercise.
Role-specific screening
What we assess
The main scoping challenge is deciding which skills must coexist in one person. We define the primary responsibility before adding adjacent capabilities.
Modelling judgement
Useful baselines and valid evaluation
Role-specific screening criteria agreed during intake
Product delivery
Integration, monitoring and iteration
Role-specific screening criteria agreed during intake
Scope fit
Demonstrated depth and realistic breadth
Role-specific screening criteria agreed during intake
A broad title should not hide conflicting expectations. The shortlist states which responsibilities each candidate has demonstrated and which require further assessment or support from your team.
FAQ
Frequently asked questions
Get started
Tell us about your open role
Share your AI/ML engineer brief. We agree scope, budget and timing with you; the 5-business-day first-shortlist timeline starts after the brief is approved and the mandate is signed.
Pay on placement
No upfront fee on the Standard plan
90-day guarantee
On Standard: free re-search if hire leaves
Human review
Recruiters review the screening evidence
Also on Recruo
Roles we hire for
Hire by location
Compared to other agencies
Further reading
Hire in the UK