The Interview Loop
Before you study a single concept, map the terrain. AI engineer loops vary more than traditional SWE loops — a frontier lab, an AI-native scaleup, and an enterprise team will test you in genuinely different ways — but they assemble from a small set of recurring rounds. Learn the eight archetypes and you can predict almost any loop from the job description.
The single most important thing to know going in: most loops are bimodal. They pair at least one AI-specific round (build a RAG pipeline, design an agent, defend an eval strategy) with at least one classic round (a data-structures problem, a behavioral panel). Preparing for only one half is the most common way strong builders fail.
The eight round archetypes
Recruiter / mission screen
universal
Thirty minutes on fit and motivation. At the labs, genuine interest in the mission and a coherent view on AI safety is a real signal, not a formality.
Practical coding screen
not pure LeetCode
Build a small API, an iterator, a spreadsheet-with-formulas, a text editor — or extend a codebase. Tests whether you write clean, working code on a realistic task, not whether you memorized algorithms.
Classic DSA round
still here
Trees, graphs, binary search, an in-memory database, an LRU cache. Alive and well even at AI-first labs — sometimes the same problem dressed in an AI-systems costume.
Take-home / build
often decisive
A multi-hour or multi-day build (sometimes paid), defended in a follow-up walkthrough. Build a RAG bot, an agent, an eval harness. The most production-realistic signal a loop can collect.
AI / LLM system design
the ADEPT round
Design a RAG system, an agent, an eval platform. RAG, orchestration, evals, cost and latency tradeoffs — run the ADEPT framework here.
Project deep-dive
often the hardest
Present and defend real work. Why this choice, what broke, what you would change. At OpenAI this is reported as the round people most underestimate.
Customer / discovery sim
forward-deployed only
A roleplay where you do real sales discovery, not engineering. Distinctive to FDE roles (Anthropic Applied AI, Harvey). High-fail because candidates treat it as a tech interview.
Behavioral / values
culture gate
Values and collaboration — Netflix's culture rounds, Scale's "Credo," lab safety-and-ethics discussions. Often gates the offer regardless of technical scores.
The 2025–26 shift: AI-assisted coding
The biggest recent change is that several companies now have you code with an AI model in the room — and grade you on how well you drive it. This inverts the old signal: it is no longer "can you write this from memory" but "can you specify, review, and verify faster than the model can mislead you."
As of late 2025, Meta replaced one traditional coding round with an AI-enabled one: a CoderPad with a built-in assistant (defaulting to Llama, switchable to other frontier models mid-interview). The format is usually one thematic problem in escalating parts — review and fix a bug, add a feature, then handle edge cases and scale — over a codebase larger than you could write by hand in the time. Because the model can produce volume, the bar rises on code review and verification: you are expected to find what the AI got wrong and justify the design. Sierra, Cursor, and Notion run variations of the same idea.
Real loops, by archetype
The cleanest persona-matched loops — the ones hiring AI builders rather than researchers — cluster at the labs' applied teams and the AI-native scaleups. A few documented shapes:
What separates scaleups from incumbents
A useful heuristic when you don't know the loop: scaleups grill on agents and evals; incumbents grill on RAG and integration. A young AI-native company wants to know you can build an autonomous system and prove it works. A large enterprise wants to know you can wire an LLM into existing infrastructure without breaking it.
| Frontier lab (applied) | AI-native scaleup | Enterprise / big tech | |
|---|---|---|---|
| Practical coding | yes | yes | yes |
| Classic DSA | sometimes | rare | yes |
| Take-home / build | yes | yes | sometimes |
| AI system design | yes | yes | yes |
| Customer / discovery sim | FDE roles | FDE roles | no |
| Eval-heavy design | yes | yes | sometimes |
| Safety / values gate | yes | yes | yes |
A field note on the round people underestimate
The coding was fine. System design was fine. Where candidates lose us is the project deep-dive — we ask them to walk through something they built and then we push: why this database, why this chunk size, what happened when it failed in production, what would you do differently. People who actually shipped the thing light up and go deeper. People who supervised it or read about it run out of road in about four minutes. You cannot fake operational scar tissue, and that is exactly what we are probing for.
The loop is not one skill tested five ways. It is five different skills, and the rejection usually comes from the one you did not prepare — not the one you did.
- Drill a practical coding task AND a classic DSA problem — the bimodal trap catches people who prepped only one.
- Have two or three real projects you can defend to four levels of "why" — the deep-dive rewards depth you cannot improvise.
- If the role says forward-deployed, rehearse the customer conversation as seriously as the system design. It fails more candidates than the code.
Now learn the script for the design round: the ADEPT Framework.