The title is often ambiguous
‘ML engineer’ can mean research, application development, platform engineering, or deployment. The search has to begin with outputs, stack, and technical depth.
[ AI and machine learning recruitment ]
Recruit the people who turn models into dependable systems.
AI teams need more than model-building ability. Production systems depend on data, infrastructure, evaluation, safety, and product judgment. Reso structures each search around the work to be done and coordinates recruiters with reach into the relevant technical communities.
The model is suitable for AI-native companies and established organizations building applied ML products, internal platforms, evaluation functions, or safety and governance capability.
Start a search01
Search coverage
Roles within the search.
Role titles vary by employer. This list describes common search territory, not a fixed boundary.
- Applied scientists and ML engineers
- ML platform and MLOps engineers
- Data engineers and data-platform leaders
- Model evaluation and red-team specialists
- AI safety and governance professionals
- Computer vision and perception engineers
- Language-model and retrieval engineers
- Technical AI product leaders
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Why it is difficult
What makes this a specialist search.
Evidence matters more than buzzwords
A strong profile shows what a person built, evaluated, deployed, or governed and at what scale—not simply a list of current model names.
Candidate markets overlap imperfectly
Researchers, infrastructure engineers, safety specialists, and technical product leaders participate in different networks and respond to different role narratives.
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Brief design
What Reso clarifies before sourcing.
The brief is the operating specification for the recruiter network. Clear evidence and constraints keep parallel search aligned.
- 01The product or research problem and expected outcomes
- 02Research-to-production balance and ownership boundaries
- 03Data, model, infrastructure, and deployment environment
- 04Required evidence: papers, systems, scale, evaluation, or leadership
- 05Location, work authorization, compensation, and interview design
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Direct answers
Questions about this search.
Does Reso recruit AI researchers or production engineers?
Potentially both. They are treated as different candidate markets with different evidence. The brief should state whether the need is research, applied modeling, infrastructure, product delivery, evaluation, or a combination.
How do you avoid keyword-only screening?
The brief is translated into observable evidence: systems built, research contribution, deployment constraints, evaluation work, data scale, ownership, and decisions made. Recruiters source and qualify against that evidence.
Can the search include adjacent backgrounds?
Yes, when the underlying capability transfers. Reso can define adjacent evidence explicitly so the search expands thoughtfully instead of lowering the bar or relying on title matching.