Interview Prep
Technical topics, coding challenges, behavioral questions and system design — everything to land your AI role.
Preparing for a machine learning interview means covering four surfaces at once: technical ML topics, coding, behavioral and system design. This page organizes all of it into ten tabs — a 200+ question bank with answer key points and follow-ups, real questions tagged by company and difficulty, and curated prep resources — so AI interview questions stop being a guessing game.
Two tabs go beyond the candidate's view. The interviewer's view shows what real interviewers at big tech, startups and research labs reward and flag, and the hiring-process tab maps timelines, rounds and pass rates per company. A dedicated China section covers the bǐshì written test that US processes usually skip. Start with the technical topics tab and work across.
Explore: ML interview questions · ML interviews at top companies · How to prepare
Machine Learning Fundamentals
Supervised Learning
- Explain bias-variance tradeoff
- What is regularization and why is it important?
- Difference between L1 and L2 regularization
- How do you handle imbalanced datasets?
Model Evaluation
- When to use precision vs recall?
- Explain cross-validation and its types
- How to evaluate regression models?
- What is the difference between accuracy and F1-score?
Feature Engineering
- How do you handle categorical variables?
- When to normalize vs standardize?
- Explain PCA and when to use it
- How do you detect and handle outliers?
Deep Learning
Neural Networks Basics
- Explain backpropagation
- Why use ReLU over sigmoid?
- What is vanishing gradient problem?
- Compare SGD, Adam, and RMSprop
CNNs
- How does convolution work?
- Explain pooling and its types
- What are skip connections?
- When to use transfer learning?
Transformers & LLMs
- Explain self-attention mechanism
- What are positional encodings?
- Difference between BERT and GPT
- How does multi-head attention work?
LLM Applications
Prompt Engineering
- Best practices for prompt engineering
- How to reduce hallucinations?
- Explain few-shot prompting
- When to use chain-of-thought?
RAG (Retrieval-Augmented Generation)
- How does RAG work?
- Explain vector embeddings
- What is semantic search?
- How to improve RAG accuracy?
Fine-tuning
- When to fine-tune vs prompt engineer?
- Explain LoRA
- What is RLHF?
- How to prepare fine-tuning data?
MLOps & Production
Model Deployment
- How to deploy ML models?
- What is model serving?
- Explain A/B testing for models
- How to monitor model performance?
Scalability
- How to scale ML systems?
- Explain data parallelism vs model parallelism
- How to optimize inference speed?
- What is model quantization?
FAQ
Common questions, answered with the data on this page.
What questions are asked in a machine learning interview?
Expect a mix of ML theory, coding, behavioral and system design. The question bank above holds 200+ curated questions with answer key points and follow-ups, grouped by category and difficulty, and the company-questions tab shows real questions asked at specific companies. Our machine learning interview questions guide adds full sample answers.
What are ML interviews at top companies like?
Multi-round and staged: the hiring-process tab maps each company's timeline, round sequence and pass rates, while the interviewer's-view tab shows what interviewers at big tech, startups and research labs count as top qualities and red flags. Our guide to ML interviews at top companies walks through a full loop.
How should I prepare for an ML engineer interview?
Work the tabs left to right: shore up technical topics, drill the coding challenges, rehearse behavioral stories, then practice the system-design problems. The resources tab lists prep books, courses and practice platforms for each stage. A week-by-week plan is in our ML engineer interview preparation guide.
How are AI interviews in China different?
The China interview tab covers the key difference: a written coding test (bǐshì) that US processes usually skip, plus Chinese-style algorithm and AI questions, behavioral rounds and offer negotiation. If you are interviewing in both markets, prepare for the written test separately — it has its own format and timing.