Interviews for AI and machine learning roles combine software engineering, statistics, machine learning theory and practical judgement. The exact mix depends on the role and company, but most processes follow a recognizable pattern. Preparing for each stage deliberately makes a big difference.

Know which role you are interviewing for

  • ML engineer: building, training, deploying and monitoring models; strong coding and systems.
  • AI engineer: building products on foundation models — prompting, RAG, agents, evaluation.
  • Data scientist: statistics, experimentation, analysis and modelling for business questions.
  • Research scientist / engineer: depth in ML theory, papers and experiments.

See AI job roles explained for more detail.

The typical stages

1. Recruiter or hiring manager screen. Your background, motivation and the role’s expectations. Prepare a two-minute story of your experience and why this team.

2. Coding. Algorithms and data structures in Python for engineering roles, often more practical data manipulation (pandas, SQL) for data science. Practise writing clean, tested code while explaining your reasoning.

3. ML fundamentals. Expect questions such as:

  • bias–variance trade-off, overfitting and regularization;
  • evaluation metrics (precision, recall, ROC-AUC, calibration) and when each matters;
  • how gradient descent and backpropagation work;
  • handling imbalanced data, data leakage and train/validation/test splits;
  • for LLM roles: tokenization, attention, fine-tuning versus RAG, evaluation and hallucinations.

4. ML system design. Design an end-to-end system: a recommendation feed, fraud detection, a document Q&A assistant. Interviewers look for clear requirements, data sources, model choice, evaluation, serving, monitoring and trade-offs — not a single “right” answer.

5. Project deep dive. Walk through a project you built: the problem, data, decisions, what failed, results and what you would do differently. Expect probing follow-ups.

6. Behavioural interview. Collaboration, handling ambiguity, disagreements, mistakes. Use the STAR structure (Situation, Task, Action, Result).

How to prepare

  • Refresh fundamentals with a structured resource such as Google’s Machine Learning Crash Course.
  • Study interview-specific material; Chip Huyen’s free Introduction to Machine Learning Interviews book covers the process and typical questions in depth.
  • Practise system design aloud with a friend or an AI assistant acting as interviewer, and ask for feedback on structure.
  • Prepare two projects you can discuss in depth, with numbers.
  • Read the company’s work: blog posts, papers, products and recent news, so you can connect your skills to their problems.
  • Prepare questions for them about data, team structure, evaluation practices and how models reach production.

Common mistakes

  • Jumping to a complex model before clarifying the problem and the metric.
  • Ignoring data quality, leakage and evaluation design.
  • Describing projects without measurable results.
  • Memorizing answers instead of reasoning through trade-offs.
  • Using AI tools during live interviews when they are not allowed.

After the interview

Write down the questions you were asked and where you struggled, then study those topics. Interviewing is a skill that improves quickly with practice. See also how to start a career in AI.

A four-week preparation plan

  • Week 1: fundamentals refresh — metrics, overfitting, gradient descent, evaluation design.
  • Week 2: coding practice daily, plus SQL and data manipulation for data roles.
  • Week 3: two system design mock interviews and polishing your project stories.
  • Week 4: company research, behavioural answers, and full mock interviews end to end.

On the day

Test your setup for remote interviews, keep water and paper nearby, think aloud, ask clarifying questions before solving, and if you get stuck, say what you would try next — interviewers value a clear reasoning process as much as the final answer.

What to watch next

  • Interviews adapting to AI coding assistants.
  • More LLM and evaluation questions for AI engineer roles.
  • Take-home projects replacing some live coding rounds.
  • Skills-based hiring for career changers.

Key takeaways

  • AI interviews combine coding, ML fundamentals, system design, project deep dives and behavioural rounds.
  • Prepare differently for ML engineer, AI engineer, data scientist and research roles.
  • In system design, clarify requirements and discuss data, evaluation, serving and monitoring.
  • Have two projects ready to discuss in depth with measurable results.
  • Practise aloud and review your weak spots after each interview.

Frequently asked questions

Do AI interviews still include LeetCode-style questions?

Many engineering roles still include algorithm coding, though some companies favour practical or take-home tasks.

How much math will I be asked?

Usually probability, statistics and basic linear algebra and calculus concepts, more for research roles.

Can I use AI assistants to practise?

Yes, they are useful mock interviewers. Just do not use them during interviews unless explicitly allowed.