The AI Engineering Interview Process

This is chapter 2 of The AI Engineering Field Guide.

You polish your CV, you send the application, and then the uncertainty starts. You don't know how many rounds to expect, whether the coding rounds lean LeetCode or RAG, or if a take-home is coming. Most companies never tell you, so you walk into the process blind.

To fix that, we went through 1,765 AI engineer job descriptions to find the ones that spell out their hiring process. We combined them with interview reports from candidates on Reddit, X, and personal blogs. Together they map what happens between "we received your application" and "here's an offer".

Most companies keep their process secret

Of the 1,765 job descriptions we analyzed, only about 80 (roughly 4.5%) include a structured interview process, and those come from 51 unique companies. Everyone else leaves you guessing.

There's a silver lining: the companies that publish their process tend to be detailed about it, and the same patterns repeat across those 51 companies. You can use those patterns to prepare for almost any AI engineering interview.

The typical interview process

The median published process has 4 steps, and most companies land in the 3-5 range. A few run lean with 2 stages, and a few stretch to 7.

The same steps show up again and again:

• Recruiter or talent screen, usually 15-30 minutes
• Technical interview: live coding, system design, or code review
• Hiring manager interview, a 45-60 minute deep dive
• Behavioral interview about values and culture
• Take-home challenge, typically 2-3 hours
• Panel interview with multiple interviewers
• A final CEO or founder interview, usually 15-30 minutes

A few published processes show how the steps combine.

Doctolib hires a Senior AI Engineer in five steps:

• recruiter interview
• feature building interview
• AI system design interview
• behavioral interview
• reference check and offer

PostHog swaps the usual final round for something unusual. After a talent partner call, a 60-minute technical interview, and a 15-minute co-founder call, candidates spend a full paid SuperDay doing the actual job.

FlowFuse runs six steps for a Full Stack Developer (AI) role. Its take-home runs 2-3 hours with AI tools encouraged, and it's followed by a 60-minute technical review with 2-3 team members.

Candidate reports

The published data covers only the 4.5% of companies that share their process, so we also collected candidate reports from Reddit, X, and personal blogs.

The reports confirm the published data and add detail on what each round looks like:

• Recruiter screen, 15-30 minutes: basic fit and, quite often, salary expectations
• Technical or coding round, 45-60 minutes: LeetCode-style problems, sometimes with an AI flavor
• AI/ML deep-dive, 45-90 minutes: RAG, hallucinations, fine-tuning versus prompting
• Take-home or project round, 1-7 days: build a RAG or agent system, or a multi-day assignment
• System design, about 60 minutes: scaling LLM apps, cost and latency optimization
• Behavioral, 30-60 minutes: STAR format, ownership in ambiguous AI work

Not every company runs every round, so expect 3-6 rounds spread over 2-6 weeks in total.

A few of these reports show where interviews are heading.

Microsoft ran an Applied AI/ML intern loop with three 45-minute rounds. In the first, you solve problems with ChatGPT, and then the interviewer modifies the problem and asks you to re-prompt. The second is raw coding with no AI tools allowed. The third combines behavioral and technical questions.

Amazon's GenAI Innovation Center loop mixes old and new. The phone screen pairs a LeetCode problem with a practical ML question: cosine similarity in NumPy. Amazon applies its standard SDE coding bar because it has no dedicated MLE job family. A GenAI depth round follows with LLM and vision architectures, fine-tuning, use case ideation, and ROI estimation. Leadership Principles questions run through the entire loop.

Eightfold.ai went a step further and let an AI agent conduct the coding round. The agent ran about 60 minutes, asked two questions, and followed up on edge cases and complexity. Then came a three-day take-home to build an AI agent and a DSA-focused interview with the engineering manager.

LangChain keeps it short, asking candidates to complete a take-home to develop an agent, discuss the solution, and finish with an applied system design interview.

IBM's technical interview runs 75 minutes and covers:

• Python, SQL, and Git
• project deep-dives
• ML and MLOps

It's followed by a 45-minute live coding session with three easy-to-medium questions on a shared whiteboard.

Mistral AI goes the longest with seven rounds:

• LLM theory
• coding
• a past project deep-dive
• a technical manager interview
• ML system design
• a take-home
• a value talk

Differences from classic ML and data science interviews

If you interviewed for data science or ML roles a few years ago, parts of this will feel familiar, and parts won't.

DSA hasn't gone away, and it still gates many loops. Amazon applies its standard SDE coding bar, IBM asks three live coding questions, and coding rounds keep showing up across candidate reports. Don't skip it in your preparation.

The AI-specific layer on top is new:

• Deep dives into LLM topics: architectures, temperature and sampling strategies, hallucinations, fine-tuning versus prompting tradeoffs
• System design for LLM apps: scaling, cost, and latency, not only data pipelines
• Evaluation questions: how you measure quality, and which metrics make sense beyond perplexity
• Project deep-dives: your resume projects get dissected, so you need to own every decision in them

The rules around AI tools are also in flux. Microsoft dedicates one round to how well you direct ChatGPT, and the next round bans the tools entirely. FlowFuse encourages them in the take-home. Ask your recruiter what's allowed in each round. Guessing wrong is an easy way to lose an offer.

Preparing for the interview

If I were preparing today, this is the list I'd work through:

• Keep your DSA sharp. IBM's live coding used three easy-to-medium questions, and that level is a good target for most coding rounds.
• Prepare to walk through your projects in depth: architecture choices, tradeoffs, what broke and how you fixed it.
• Rehearse the standard LLM talking points: RAG design, hallucination handling, and when fine-tuning beats prompting.
• Take take-homes seriously. Most are scoped at 2-3 hours, but agent-building assignments can run to several days. If AI tools are encouraged, use them and be ready to explain how.
• Prepare STAR stories, especially about ownership in ambiguous situations. AI work is often undefined, and interviewers probe how you handle that.
• Expect a values or culture round even at small companies, where founder interviews are short but real.

The always-fresh version of the data

I adapted this chapter from the open-source AI Engineering Field Guide. The per-company interview process breakdowns and the links to the original candidate reports are all in the repo. I update them as new reports come in, and the numbers there are always fresher than the ones printed here.

In the previous chapter I covered what the job involves: What Does an AI Engineer Do?. Up next: After the Interview: Offers and Negotiation.

Build this

The best preparation for the coding and take-home rounds is a project you can walk through and a system you designed yourself. Pick one from the projects page, build it past the demo stage, and it doubles as interview material. For structured practice, the interview hub organizes preparation material by round type and includes question banks for theory, coding, and system design. And when you're ready for the negotiation chapter, become a member to unlock it.