What to assess in an AI engineer
This guide is for engineers who build applications with language models, not researchers who train them. Model training and Python-heavy machine learning work are covered in conversation; hands-on code is TypeScript.
- Application design: retrieval, tools, prompts, structured output and where each fits.
- Evaluation: building a test set, measuring quality, and catching regressions when a prompt or model changes.
- Failure handling: timeouts, refusals, malformed output, retries and fallbacks.
- Trust boundaries: treating user input and retrieved text as data, and defending against prompt injection.
- Cost and latency: model choice, caching and streaming.
- Their own AI fluency: how they use a coding assistant while building with models.
Screening questions for AI engineers
Design questions can open a whiteboard; code questions open a TypeScript workspace on Node, and the server reruns the tests on each answer.
- Sketch a support assistant that answers from your help centre. How do you stop it making things up?
- How would you know whether a prompt change made answers better or worse?
- A user pastes text telling your agent to ignore its instructions. What protects you?
- Write a function that parses a model's JSON output and handles every way it can be wrong.
- When would you choose a smaller, cheaper model, and how would you check it is good enough?
The AI engineering coding round
A Node.js service or a Next.js app suits AI engineering tasks such as parsing model output, building a retrieval step or adding a tool. The candidate works in a Cursor-like IDE with a terminal, tests, completions, file-aware chat and an agent.
Run it unguarded: an AI engineer should be fluent with an assistant. Code is tinted by authorship, and the server reruns the tests when the round ends.
AI-fluency signals for an AI engineer
| Signal | What it tells you about an AI engineer |
|---|---|
| Time to first prompt | Whether they designed the approach before asking the assistant to build it. |
| Prompt quality | Whether their prompts to the coding assistant are as careful as the prompts they ship. |
| AI-generated share | How much was delegated to the assistant. |
| Verification | Whether they tested generated code, especially parsing and error paths. |
| Over-reliance flags | Large accepted blocks never reviewed or tested. |
| Recovery | What they did when the assistant's code mishandled a bad model response. |
From scores to a decision
Fundamentals and AI fluency are scored separately, with every score citing an answer or edit. The recommendation is advice, and a named person signs off every round.
Questions
Can SynergicHire assess machine learning research skills?
Only in conversation. It suits engineers who build applications with language models. Hands-on code is TypeScript, not Python notebooks.
Which coding round stack suits an AI engineer?
A Node.js service or a Next.js app, where the task can involve parsing model output, retrieval or tool calls.
Does SynergicHire itself use AI to assess candidates?
Yes. The AI assesses and cites the evidence for every score. A named person makes every decision, and candidates are told this before they start.
How is prompt quality measured?
From timestamped events: how specific prompts are, what context is attached, and how many near-identical re-asks it took.