How to Prepare for an AI Job Interview, Technical or Not

An AI job interview usually tests three things, whatever the role: can you do the core job, do you understand how AI products behave (uncertain outputs, evals, cost and latency), and do you use AI tools with judgment. To prepare, practice one system design or product walkthrough, learn to explain how you'd measure quality, and bring a real workflow you've built.

Definition: An AI job interview is the hiring process for a role at an AI company or on an AI team, which typically adds AI-specific rounds (model behavior, evaluation, system design or AI product questions) to the standard coding, case or behavioral interviews for that function.

Everyone claims to use AI now. Interviews are where that claim gets tested.

The good news is that the test is learnable. The questions repeat more than you'd think, and the strongest answers tend to share one habit: they talk about how to know whether the AI is working, not just how to make it work.

What an AI job interview looks like now

The process at AI companies mostly looks like any tech loop, with AI woven through it. OpenAI's public interview guide lays out five stages: resume review, introductory calls, a skills-based assessment (pair coding, a take-home or a technical test, depending on the team), final interviews of four to six hours with four to six people over one or two days, and a decision.

The difference is in what each round probes. AI fluency is now tested well beyond engineering roles:

Demand is broad. The Stanford AI Index 2026, using Lightcast data, puts AI job postings at about 2.6 percent of all US postings in 2025, and mentions of generative AI skills in AI postings grew 111 percent from 2024 to 2025.
It's not just tech jobs. Lightcast's July 2025 analysis of more than 1.3 billion postings found that in 2024, 51 percent of postings asking for AI skills were outside IT and computer science, and that postings listing AI skills advertised salaries about 28 percent higher.
Managers say they screen for it. In Microsoft and LinkedIn's 2024 Work Trend Index (31,000 knowledge workers in 31 markets, surveyed February to March 2024), 66 percent of leaders reported they wouldn't hire someone without AI skills.

So a marketing candidate at an AI startup can expect AI questions. So can a recruiter. If you browse the AI track of the 1752vc careers board, you'll see how many non-engineering roles sit on AI teams.

Can you use AI during the interview?

It depends on the company and the round, so ask. Anthropic's candidate guidance encourages using Claude to research the company and practice, but asks candidates not to use AI in take-homes or live interviews unless the team indicates otherwise. OpenAI's guide notes that some formats intentionally allow AI tools while others are designed to assess independent problem-solving.

Our read: assume no AI in live rounds unless someone confirms it's allowed, and ask before every take-home. A clear question costs nothing. A wrong guess can cost the job.

Technical AI interview questions, round by round

For engineering, ML and research roles, expect some mix of these. Our guide on how to become an AI engineer covers the skills behind them.

Coding. Many loops still include standard data structures and clean Python. In our view, AI tools haven't so much removed this round as shifted it toward reading, testing and explaining code.

ML fundamentals. Knowledge questions for ML-heavy roles. Chip Huyen's open Introduction to Machine Learning Interviews Book separates these knowledge questions from open-ended design questions, which is a useful way to plan your study. Sample questions: - Why might a model do well on validation data and poorly in production? - What's the trade-off between precision and recall for a fraud model? - When would you fine-tune a model instead of improving retrieval or the prompt?

System design. Design an AI feature end to end (worked example below).

Evals. How you'd know the system is good, and how you'd know it got worse.

Debugging. A pipeline that fails on certain inputs. Talk through how you'd isolate the cause.

Your projects. Expect deep follow-ups: why that model, what broke, what you'd change.

Worked example: an LLM system design answer in six moves

Prompt: Design an assistant that answers employee questions from a company's 50,000 internal documents.

Clarify. Who asks, how many questions a day, how accurate it needs to be, and what happens when it's wrong. Assume 20,000 questions a day and that a wrong HR policy answer is costly.
Start simple. Retrieval over the documents plus one model call with citations. Anthropic's engineering guide on building agents (December 2024) makes the case for starting with the simplest design that works and adding complexity only when it clearly helps, and interviewers tend to reward that instinct.
Retrieval. Chunk the documents, use keyword plus semantic search, respect permissions so people only see documents they're allowed to.
Evals. A test set of real questions with expected answers, checks for citation accuracy, and a rule that the assistant answers "I don't know" when sources are missing.
Cost and latency. Do the math out loud (below).
Failure handling. Logging, a feedback button, human escalation for sensitive topics, and a plan to add every reported failure to the test set.

The cost math. Using hypothetical prices of $3 per million input tokens and $15 per million output tokens, a query with 3,000 input tokens and 500 output tokens costs about $0.009 plus $0.0075, or $0.0165. At 20,000 queries a day, that's about $330 a day, or roughly $9,900 over 30 days. Real prices vary widely by model and change often. The point is showing you'd check whether cheaper models, shorter context or caching could cut that bill before anyone asks.

How to answer eval questions

Eval questions ("How would you know this feature is good?") come up for engineers, product managers and increasingly for go-to-market roles. For product managers the stakes are rising: in a 2025 guest guide for Lenny's Newsletter, product leader Aman Khan argued that writing good evals is becoming the defining skill of the AI PM. One structure, which tracks the steps in OpenAI's evaluation best practices guide:

Define the objective from the user's side: correct, cited, safe, fast.
Build a dataset from real inputs plus edge cases.
Pick metrics and graders for each criterion: exact checks where possible, a model grader for judgment calls, human review for a sample.
Compare versions before shipping a change.
Keep evaluating in production, adding failures as new test cases.

One detail that tends to impress: OpenAI's guide cautions against relying on automated model graders without calibrating them against human labels. Mentioning that you'd check your grader against human judgments shows you've done this for real.

AI product managers get their own flavor of these questions; see our guide on how to become an AI product manager.

Non-technical AI interview questions

For sales, customer success, operations, marketing, recruiting and policy roles at AI companies, the AI questions are about judgment, not math. Samples, with what we think they test:

Walk me through how you use AI in your work today. Whether you have a real workflow or a vague habit.
Tell me about a time AI gave you a wrong answer. What did you do? Whether you understand outputs can be confidently wrong, and whether you check.
A customer claims our product hallucinated. How do you respond? Honesty, product knowledge and escalation judgment.
Where would you not use AI in this role? Restraint. Good answers name privacy, high-stakes decisions or relationship moments.
How would you explain what our product does to a skeptical buyer? Plain language without hype.
What AI news or product release changed your thinking recently, and why? Curiosity and whether you follow the field.

The general structure of these loops (behavioral stories, product and metrics questions) is covered in our guide to tech interviews for non-technical roles. For sales seats specifically, see how to get a sales job at an AI company.

How to demonstrate AI fluency without buzzwords

AI fluency isn't knowing the latest model names. It's showing you can get reliable work out of AI and know where it breaks.

What tends to convince an interviewer:

A real workflow with a before and after. "I built a prompt chain that drafts first-pass support replies; it cut my response time from about ten minutes to four, and I still edit every one."
A failure you caught. The time it invented a statistic, and the check you added.
Cost and quality awareness. Knowing that a bigger model isn't automatically better for every task.
An artifact. A small project, a write-up or a short demo you can share on screen.

"Isn't prompt engineering the key AI skill?"

It's the most talked-about one, and writing clear instructions for a model is genuinely useful.

But.

Prompts are the easiest part to copy and the quickest to go stale as models change. In our view, what interviewers probe harder is judgment around the prompt: how you'd test the output, catch errors and decide whether AI belongs in the task at all. Show the prompt if you like. Spend more time on how you knew it worked.

A one-week AI job interview prep plan

An illustrative plan for about 90 minutes a day.

Day	Technical candidates	Non-technical candidates
1	Read the company's docs, API or product; note one weakness	Use the product; write how you'd explain it to a buyer
2	Two coding problems, talked through out loud	Draft six behavioral stories, two involving AI
3	One system design walkthrough with cost math	Write up one AI workflow you use, with a before and after
4	Design an eval for the company's main feature	Prepare a "wrong answer" story and a "where I wouldn't use AI" answer
5	ML fundamentals review for your role	Read the company's recent posts and one competitor's
6	Mock interview, full loop	Mock interview, full loop
7	Fix the weakest answer; prepare questions for them	Fix the weakest answer; prepare questions for them

Good questions to ask them: how they evaluate their models or features today, what broke most recently, and what would make this hire a success in six months. At a startup, ask about runway too, and whether the product would survive the next model release. Our guide to AI startup moats explains why that matters.

Common mistakes in AI job interviews

Buzzword answers. "Agentic, multimodal, RAG" without a concrete example tends to fall flat.
Skipping evaluation. Designing a system without saying how you'd measure it leaves out the part many interviewers care most about.
Overbuilding the design. Jumping to multi-agent systems when one model call with retrieval would do.
Guessing the AI policy. Using an assistant in a round where it isn't allowed can end the process.
Hiding uncertainty. Saying how you'd find out often beats an invented answer.

Where we land

We think the most reliable way to stand out in an AI job interview is to sound like someone who has shipped something and measured it. That's true for engineers designing systems and for account executives explaining a pilot.

It's one view, and loops vary a lot between frontier labs, AI startups and AI teams inside older companies. Our pillar guide on how to get a job in AI covers those differences.

When you want more interviews to practice on, the AI track of the 1752vc careers board lists roles at AI companies, refreshed every week with each employer's own posting date. Filter by level and by Past 7 days to see what's new, and pick two or three to prepare for properly.

The bottom line

AI interviews reward people who can explain how they'd know the AI is working. That's the skill under every round.

Anyone can say they use AI.

Fewer can show you how they checked it.

Key takeaways

An AI job interview typically tests your core job skills, your understanding of how AI products behave and your judgment with AI tools.
Technical loops often cover coding, ML fundamentals, LLM system design, evals and deep dives on your projects.
For system design, start simple, cover retrieval, evals, cost and failure handling, and do the cost math out loud.
Non-technical AI interviews tend to probe a real AI workflow, a time AI was wrong and where you wouldn't use it.
AI tool policies differ by company and round, so ask before any take-home and assume no AI in live rounds unless the company allows it.

Frequently asked questions

What questions are asked in an AI job interview?

It depends on the role. Technical candidates commonly face coding, ML fundamentals, system design ("design an assistant over company documents") and eval questions ("how would you know it got worse?"). Non-technical candidates are often asked how they use AI today, about a time AI gave a wrong answer, where they would not use AI, and how they'd explain the product to a skeptical buyer.

How do you prepare for an LLM system design interview?

Practice one end-to-end walkthrough out loud: clarify users, volume and the cost of errors, start with the simplest design, then cover retrieval, evaluation, cost and latency math, and failure handling such as logging and human escalation. Interviewers tend to reward a simple design that's measured well over a complex one that isn't, so leave time for evals.

How do you answer eval questions in an AI interview?

Define success from the user's side, build a test set from real inputs plus edge cases, choose a metric and grader for each criterion, compare versions before shipping, and keep testing in production by adding failures to the set. Mentioning that you'd calibrate automated model graders against human judgments, as OpenAI's eval guidance suggests, shows practical experience.

Can you use ChatGPT or Claude during an interview at an AI company?

It varies by company and round, so ask. Anthropic's candidate guidance encourages using AI to research and practice, but asks candidates not to use it in take-homes or live interviews unless the team indicates otherwise. OpenAI's guide notes some formats allow AI tools and others test independent problem-solving. Assuming no AI in live rounds is the safer default.

How do you show AI skills in an interview for a non-technical job?

Bring one concrete workflow with a before and after, such as cutting a task from ten minutes to four while still checking every output. Add a story about catching an AI mistake and the check you added, and be ready to say where you wouldn't use AI. A small artifact you can share on screen tends to beat a list of tools.

Sources

Disclaimer: This guide is for general education only and is not legal, tax or investment advice. Laws, market data and program terms change, so it may not reflect the latest developments or fit your situation. Treat it as a starting point, not a source of truth, and talk to a qualified lawyer, accountant or financial adviser before you make decisions.