Your first AI engineer should own one product outcome, not every task that contains the word AI. Start with the feature, model, or workflow the company must deliver in the next 90 days. Then choose the engineering discipline, evidence, and interview loop that match that result.
Reviewed August 3, 2026.
A broad title attracts broad resumes. A brief that names the user, product behavior, data boundary, quality measure, and production owner gives candidates enough detail to decide whether their experience fits.
Start with the blocked product outcome
Write one sentence that connects the user problem to a measurable release. For example: “Ship a support assistant that answers from approved product documentation, cites its sources, and hands uncertain questions to an agent.” That sentence tells a candidate more than “build our AI platform.”
The outcome also clarifies the role. Use the following guide before choosing a title.
| Primary outcome | Role to assess | Evidence to request |
|---|---|---|
| Ship a model-powered product feature | AI engineer or AI product engineer | Production application code, model integration, evaluation, fallback behavior, and user feedback |
| Train and operate a predictive model | Machine learning engineer | Dataset choices, target metric, validation design, deployment, monitoring, and retraining |
| Improve retrieval or language-model quality | LLM engineer | Test sets, retrieval analysis, model tradeoffs, inference controls, and failure review |
| Make model releases reliable | MLOps engineer | Reproducible pipelines, model registry, observability, rollback, and incident response |
If the distinction is still unclear, use Crosscheck’s guide to choose the right AI engineering role. The title should follow the work.
Define the first 90 days
A startup needs a senior hire who can reduce uncertainty. Give candidates a concrete 90-day result and the constraints around it. Include:
- The user or team receiving the result.
- The current product, data, and infrastructure.
- The quality measure the team will use.
- The privacy, security, latency, and cost boundaries.
- The decisions this person can make without another approval.
- The engineer, founder, or product leader who will work with the hire.
Avoid a list that combines full-stack ownership, model research, data engineering, cloud security, and platform operations as equal priorities. Rank the work. If the company needs several disciplines, name the first phase and the likely second hire.
Decide whether you need a builder or a research specialist
Most early product teams need a software engineer who can use and evaluate models inside a production application. That person still needs AI depth. The depth should match the product risk.
A research-heavy role makes sense when product value depends on a new modeling method, proprietary training, or scientific work that existing services cannot provide. An applied role makes sense when the hard part is turning available models into a useful, reliable feature.
Do not use a degree as a substitute for evidence. Ask for research credentials when the work requires research. Ask for shipped systems when the work requires a product owner.
Ask for proof from a real system
Resume keywords show exposure. They do not show the candidate’s decisions. Ask each candidate to choose one system and explain:
- The user and product requirement.
- The model or approach they selected and the alternative they rejected.
- The test set or evaluation method.
- A failure found before or after release.
- The change they made after reviewing that failure.
- The production measure they watched after launch.
A candidate should separate personal work from team work. “We built a RAG system” needs a follow-up: What did you own, what decision did you make, and what changed because of it?
Use one practical interview exercise
Give the candidate a small version of the company’s problem. Share enough context to make tradeoffs possible. For a model-powered product feature, ask for a request flow, evaluation plan, release check, fallback path, and operating measures. For a custom model, ask for the target, dataset review, validation plan, deployment choice, and monitoring.
Score the reasoning. Do not grade the candidate against a hidden architecture. A strong candidate should state assumptions, ask about missing constraints, and explain what evidence would change the design.
NIST’s AI Risk Management Framework organizes AI work around governing, mapping, measuring, and managing risk. A startup interview does not need to reproduce that framework, but it should test whether the candidate can connect a system’s context to measurement and operating controls.
Choose contract or permanent ownership
A contract engineer can fit a defined prototype, evaluation sprint, technical assessment, migration, or delivery gap. A permanent hire fits continuing ownership of the product or model system across releases.
Do not make the engagement choice from title alone. Write down the work period, decision rights, expected handoff, and support duty. If the work ends with an artifact and a documented handoff, contract staffing may fit. If the person will set the roadmap and own incidents six months later, direct hire may fit better.
Write the hiring brief
A useful first-AI-hire brief can fit on one page:
- Outcome: the product result due in the first 90 days.
- Users: who will use or depend on the system.
- Stack: the application, data, model, and infrastructure already in place.
- Evidence: the past work candidates must explain.
- Constraints: quality, security, latency, cost, and location.
- Ownership: decisions the hire controls and work that stays with the team.
- Engagement: contract, contract-to-hire, or direct hire.
Crosscheck recruiting guidance: Bennett reviews the product outcome and screening evidence before sourcing begins. That step helps separate a founding-level title from the engineering discipline the company needs.
Review Crosscheck’s AI and machine learning recruiting practice, compare LLM engineer hiring criteria with ML engineer hiring criteria, or submit the completed brief.