13 - Interview prep: what they actually ask¶

What this session is¶

The interview loop for AI engineering roles, decoded. What each round is for, what they actually ask, and how to prep without LeetCode-burnout.

The typical loop¶

For applied AI / serving / MLOps roles in 2026:

Recruiter screen (30 min). Background, motivation, salary, work auth.
Hiring manager screen (30-45 min). Why this role, your relevant work, technical depth at conversational level.
Technical screen (60 min). One of: light coding, ML concepts, system design lite.
Onsite / virtual onsite (3-5 hours). Multiple rounds: deeper coding, ML/AI knowledge, system design, behavioral.
Offer + negotiation.

Research roles add: paper review, research presentation, possibly a take-home.

What each round actually tests¶

Recruiter screen¶

Testing: Are you a real candidate? Do you understand the role? Salary expectations realistic?

Prep: - Have a 60-second background pitch ready. - Know your salary range (use levels.fyi, Blind, payscale). - Have specific reasons for wanting this company.

Hiring manager screen¶

Testing: Are you technically credible in conversation? Do you understand AI at the level the role requires? Will you fit the team?

Prep: - Be able to describe your portfolio projects in detail. - Be able to talk about a recent paper or industry trend in your specialization. - Have 2-3 questions about the team and their AI stack ready.

Technical screen¶

Format varies wildly by company. Three common shapes:

a) Coding question. Often easier than software engineering rounds. Usually a Python problem involving strings, dicts, or simple algorithms. Sometimes implementing a small ML primitive (cosine similarity, softmax, attention from scratch). LeetCode-medium at worst.

b) ML / AI concepts oral exam. "Explain how attention works." "What's the difference between BERT and GPT?" "Walk me through fine-tuning vs in-context learning." "What's RAG and when would you use it?"

c) Pseudocode system design. "Design a RAG pipeline for [task]." "How would you serve a 70B model with strict latency requirements?"

You'll get a, b, or sometimes a+b in one screen.

Onsite rounds¶

For a typical applied AI loop:

Coding (1-2 hours): harder version of the screen. Sometimes implement a small attention or training loop from scratch.
AI/ML knowledge (1 hour): deeper oral exam. Often paper-related.
System design (1 hour): full design problem. RAG-at-scale, eval-at-scale, serving-at-scale.
Behavioral (45 min): STAR-format past-project questions.
Sometimes: take-home before onsite.

For research / fine-tuning roles, add:

Paper presentation: present a paper of your choice or one they assigned.
Research design: "How would you investigate X?"

For MLOps roles, add:

Infra design: Kubernetes, CI/CD for ML, monitoring.

What they actually ask (top 20)¶

These come up over and over:

"Explain how a transformer works."
"Explain attention. Why is it O(n²)?"
"What is KV caching? Why does it matter for inference?"
"What is quantization? When would you use it?"
"Explain RAG. What are its failure modes?"
"What's the difference between fine-tuning and in-context learning?"
"What is LoRA? Why is it efficient?"
"How would you evaluate an LLM-powered feature?"
"What's perplexity? When is it misleading?"
"What does temperature do in LLM sampling?"
"Compare DPO and RLHF at a high level."
"What's a vector database and how does it work conceptually?"
"Walk me through a debugging session for a model that won't train."
"How do you handle hallucinations in production?"
"What metrics do you use for a RAG system?"
"Design a system to serve 10M LLM requests/day with p95 latency under 1s."
"Design a fine-tuning pipeline for [domain] with weekly updates."
"Tell me about a project where you debugged something hard."
"Tell me about a time you disagreed with a teammate technically."
"Why this company specifically?"

If you can answer all 20 well, you're ready.

How to prep without burning out¶

Don't: grind 200 LeetCode problems. The coding bar for AI roles is lower than for SWE. Spending months on tree DP is wasted.

Do:

Practice 30 Python LeetCode-easy/medium problems to be loose. That's enough.
Practice the "top 20" out loud. Record yourself. Listen back. You'll cringe; that's the point.
Mock interviews. Pramp, friends, paid services. 5 mocks > 50 hours of solo prep.
Practice system design by writing up 3-5 design docs for the canonical problems. RAG, eval, serving, training pipeline, agent workflow.
Re-read your own portfolio projects. You will be asked details. Be specific.

The behavioral round¶

Underprepped by engineers. STAR format:

Situation: brief context.
Task: what was your responsibility.
Action: what you specifically did.
Result: what happened, with numbers if possible.

Prepare 5 stories that cover: hard technical problem, conflict, failure-and-recovery, leadership-without-authority, ambiguity. Rotate across questions.

Avoid: "we did X" (use "I"), vague results, blaming.

Take-homes¶

Common for AI roles. Format: "build a small RAG system / fine-tune / serving setup / eval pipeline."

Tips: - Time-box. Don't spend 40 hours on a "4 hour" take-home. - Include a README with: what you built, what you'd do with more time, limitations. - Include evals. They want to see eval discipline. - If they ask for unpaid work over 4 hours with no review-time discussion: that's a red flag.

Negotiation¶

Even on entry roles, negotiate. Companies expect it. The first offer usually has room.

Know your market band.
Get the offer in writing first.
Negotiate by email when possible. Time to think > on-the-spot.
Negotiate base, signing bonus, equity, start date, level. In that order of likely flexibility.
"Is there any flexibility on the base?" is the magic phrase.

Salary negotiation deserves its own book. Fearless Salary Negotiation by Josh Doody is the standard recommendation.

What you might wonder¶

"What if I get an LLM-coding question?" Some companies now allow / require using ChatGPT or Claude during the interview, watching how you collaborate with it. Practice this - using AI well in interviews is a separate skill. Don't pretend you "would never use AI"; that reads as dishonest.

"What if I freeze?" Common. Pause. Breathe. Say "let me think for a moment." Silence is OK. Talking-while-confused is worse.

"What if I bomb?" Most candidates bomb several interviews before landing. The data: average AI engineer offer takes 30-60 interviews across 20-30 companies. Failing isn't failure; it's the path.

"Should I take the first offer?" Usually no. Even if you accept, try to negotiate. Companies that retract over a polite negotiation aren't companies you want.

Done¶

Know the loop shape.
Know the top 20.
Have a prep plan that's not LeetCode-burnout.

Next: First 90 days on the job →