AI-native engineering hire
Hire ML platform engineers to make model delivery reliable.
Find engineers who can turn model deployment into a service your product teams can depend on. Finalists have passed all agreed technical and soft-skill stages, with interest, compensation expectations and availability confirmed.
First qualifying finalist by business day 15, or stop the search for a full $2,000 refund. The search runs for 60 calendar days from the agreed launch date while the vacancy remains open.
Standard recruitment
Your search at a glance
Standard: 18% of first-year salary. $2,000 is paid at launch and credited toward that total; the balance is due when your hire starts. We agree the salary basis and search scope before payment.
60-day search, launch-payment refund and replacement terms
- Seniority
- Senior ML platform engineer searches; the technical bar is agreed in your brief.
- Compensation guide
- Indicative planning band: €70–115K 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.
Focus your engineers’ interview time
We assess technical and soft skills before introducing candidates, so your engineers meet people who have passed the agreed requirements. Review the evidence, run your own technical and team interviews, and choose who to hire. We support coordination and the offer process.
Choosing the role
Scope the platform your team actually needs
An ML platform engineer builds and operates the systems that help teams train, deploy, monitor and update models. The brief might centre on reproducible training, batch prediction, real-time serving or a shared model gateway. It should identify the workflows the engineer will own and the teams who depend on them.
GPU inference is one possible specialism, not a requirement for every ML platform role. A team serving large models on its own infrastructure may need experience with batching, memory pressure and capacity planning. A team using managed APIs or CPU-based models may need stronger deployment automation, data lineage, observability and access control. We distinguish these scopes before sourcing.
A useful hiring trigger is persistent platform work that lacks an owner: releases need manual intervention, model versions are hard to trace, inference spend is unexplained, or feature teams cannot recover from failures independently. Team size alone does not decide whether to hire. Agree the service responsibilities and how the new engineer will work with your existing infrastructure team.
Bring to the intake: your deployment diagram, workload types, cloud or hosting constraints, reliability targets, cost concerns and on-call expectations. If the main need is improving a model’s behaviour, compare an AI/ML engineer or an LLM engineer. Platform ownership should support a defined set of users and workloads.
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 ML platform engineer
Role scope: Use this scorecard to assess whether a candidate can own the platform responsibilities in your brief and support the teams that depend on them.
- Delivery: can explain model and dependency versioning, deployment checks and an actionable rollback path.
- Reliability: defines useful service indicators and investigates failures across the request or job lifecycle.
- Capacity and cost: relates resource use to actual workload behaviour and explains how to test an optimisation.
- Developer experience: designs a platform workflow that other engineers can use and support.
- Operations: makes access controls, incident responsibilities and documentation part of the platform design.
How we source
How we assess platform ownership
We look across ML infrastructure, MLOps, platform engineering and SRE backgrounds, then assess the ML-specific responsibilities your brief requires. Targeted outreach and referrals help us find engineers with comparable operating constraints. Public speaking, an open-source contribution and experience with a particular GPU are optional unless the work makes that expertise essential.
The technical interview follows a model from build to deployment and operation. We ask how releases are reproduced, rolled back and monitored, and who can access the underlying data and artifacts. For a GPU-serving role, follow-ups can explore queueing, memory limits and load testing. For a batch platform, they can explore retries, scheduling and partial-job recovery. A human recruiter reviews the AI-supported screen before delivery.
Candidates can show their reasoning with redacted architecture diagrams, an incident walkthrough or a relevant work sample. We assess their personal decisions and ownership rather than using raw token volume as a qualification threshold. Your shortlist records the relevant operating experience, collaboration needs and areas for your platform lead to validate.
Role-specific screening
What we assess
The right platform candidate depends on the workload. We distinguish training, batch processing, managed-model integration and self-hosted inference instead of combining every infrastructure skill into one brief.
Release reliability
Reproducible deployments and rollback
Role-specific screening criteria agreed during intake
Operating judgement
Failure analysis, capacity and cost
Role-specific screening criteria agreed during intake
Platform adoption
Usable workflows and clear ownership
Role-specific screening criteria agreed during intake
We make essential experience explicit. A strong CPU or managed-service background can be relevant to one platform and insufficient for another; the shortlist explains that fit against your environment.
FAQ
Frequently asked questions
Get started
Tell us about your open role
Share your ML platform engineer brief. We agree the assessment criteria, budget, launch date and 60-day search scope before you commit.
$2,000 to launch
Fully credited toward the 18% Standard fee
90-day guarantee
One replacement search under agreed conditions
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