Tesla
Hard
Machine Learning EngineerCompare RNNs, LSTMs, Transformers, and MPC
Machine Learning
5
0
September 6, 2025
LLM & Generative AI Interview Questions
LLM and generative AI questions are rapidly growing in interview frequency as companies adopt AI-first strategies.
Expect questions on transformer architecture, attention mechanisms, fine-tuning strategies, RAG pipelines, and evaluation of generative models.
Interviewers at AI companies like Anthropic, OpenAI, and Google evaluate both theoretical depth and practical deployment experience.
525Questions
115Companies
Easy 52 • Medium 261 • Hard 198Difficulty mix
Common LLM interview patterns
- Transformer architecture and self-attention mechanism
- Fine-tuning vs prompting vs RAG trade-offs
- Retrieval-Augmented Generation (RAG) pipeline design
- Prompt engineering and chain-of-thought reasoning
- Evaluation metrics for generative models (BLEU, ROUGE, human eval)
- Tokenization strategies and vocabulary design
- Alignment, RLHF, and safety considerations
LLM interview questions
Filter and sort
Netflix
Hard
Data ScientistDesign a robust conversion propensity model
Machine Learning
9
0
October 13, 2025
xAI
Hard
Machine Learning EngineerDesign a house-price prediction workflow
Machine Learning
5
0
July 17, 2025
Apple
Medium
Machine Learning EngineerCompare DCN v1 vs v2 and A/B test
Machine Learning
6
0
March 1, 2026
Perplexity
Medium
Machine Learning EngineerTrain and improve a scikit-learn binary classifier
Machine Learning
8
0
December 15, 2025
Sealth
Easy
Machine Learning EngineerImplement Beam Search With Length Normalization
Machine Learning
10
0
April 19, 2026
Intuit
Medium
Data ScientistEngineer and Impute ZIP Features
Machine Learning
7
0
February 23, 2026
Uber
Medium
Machine Learning EngineerImplement Multi-Head Self-Attention
Machine Learning
10
0
January 10, 2026
Amazon
Easy
Machine Learning EngineerList hyperparameter tuning methods
Machine Learning
10
0
December 8, 2025
DoorDash
Hard
Data ScientistDesign and evaluate an uplift model
Machine Learning
9
0
October 13, 2025
Uber
Medium
Machine Learning EngineerImplement CLIP Contrastive Loss
Machine Learning
8
0
April 3, 2026
NVIDIA
Medium
Machine Learning EngineerExplain bias-variance, calibration, and model drift
Machine Learning
4
0
February 11, 2026
Applied Intuition
Medium
Machine Learning EngineerImplement correct attention masking
Machine Learning
4
0
September 6, 2025
Rippling
Medium
Software EngineerFind minimum of unknown convex function
Machine Learning
5
0
December 8, 2025
Meta
Hard
Data ScientistIdentify Features for Fake News Detection on Facebook
Machine Learning
30
0
August 4, 2025
Google
Medium
Machine Learning EngineerExplain GRPO-style training for diffusion models
Machine Learning
6
0
December 8, 2025
TikTok
Hard
Data ScientistDesign Real-Time Credit Card Fraud Detection System
Machine Learning
19
0
July 12, 2025
Google
Medium
Data ScientistAdjust YouTube Ad Scores Using Mixed-Effects Linear Regression
Machine Learning
10
0
July 12, 2025
Amazon
Medium
Data ScientistOptimize Predictive Analytics: Feature Engineering to Model Evaluation
Machine Learning
18
0
July 12, 2025
Amazon
Medium
Software EngineerExplain overfitting, regularization, and LLM techniques
Machine Learning
5
0
February 12, 2026
Common mistakes in LLM interviews
- Not understanding the difference between fine-tuning and in-context learning
- Ignoring hallucination risks in production deployments
- Overcomplicating solutions when prompt engineering suffices
- Not discussing latency, cost, and token budget trade-offs
- Treating LLMs as deterministic systems
How LLM questions are evaluated
Show practical understanding of when to use fine-tuning vs RAG vs prompting.
Discuss evaluation strategies for open-ended generation tasks.
Demonstrate awareness of safety, alignment, and deployment considerations.
Related ML concepts
LLM & Generative AI Interview FAQs
What is RAG and how does it differ from fine-tuning?
RAG (Retrieval-Augmented Generation) retrieves relevant documents at inference time and provides them as context to the LLM. Fine-tuning modifies the model weights on your data. RAG is better for frequently changing knowledge; fine-tuning is better for teaching the model new skills or styles.
What transformer concepts should I know for interviews?
Understand self-attention, multi-head attention, positional encoding, and the encoder-decoder architecture. Know why attention scales better than RNNs for long sequences. Be able to explain how the key-query-value mechanism works intuitively.