pallavi-shekhar/ai-engineering-interview-questions-company-wise

What it is A free, open, searchable collection of real AI-engineering interview questions drawn from publicly reported interview experiences across 35 companies. It's organized company by company, and answers are linked under each question wherever they exist. The README stresses nothing in it is confidential and that interview loops change constantly — so it's a map of what each company tends to care about, not a script of what you'll be asked.

What's inside

• Real questions from 35 companies, grouped into five buckets:
  • Frontier AI Labs — Anthropic, OpenAI, Google DeepMind, Meta, xAI, Mistral, Cohere, DeepSeek, Moonshot, Zhipu, Alibaba, Sarvam
  • Big Tech AI organizations — Microsoft, Amazon/AWS, Apple, NVIDIA, Tesla, plus consumer-scale ML companies (Uber, Netflix, LinkedIn, Airbnb, Pinterest, Spotify)
  • AI Infrastructure and Platform companies — Databricks, Groq, Together AI, Hugging Face, Scale AI, Perplexity
  • AI-Native Product companies — Cursor, Cognition, Sierra, Harvey, Glean, Character.AI, ElevenLabs, Abridge, Figure AI, Waymo
  • Forward-Deployed and Enterprise AI — Palantir
• A Common Questions section for questions that recur across many companies. Each one is listed once with the companies that ask it, so nothing is repeated in the company sections. These are grouped by topic: LLM internals and architecture; inference, serving and GPU performance; RAG and retrieval; agents and tool use; fine-tuning/post-training/alignment; evaluation and observability; safety and responsible AI; multimodal/speech; AI system design; and coding/data structures.
• Each company's section covers the roles it hires for, the interview loop as publicly reported, and company-specific questions grouped by topic.
• Linked answers, where available (many point to Outcome School's own blog posts and videos — e.g., on KV cache, FlashAttention, continuous batching, PagedAttention, RAG, agents).

Who it's for Anyone interviewing for an AI-focused role. The README lists ~15 job titles it's helpful for, including AI Engineer, Gen AI Engineer, LLM Engineer, Agentic AI Engineer, Machine Learning Engineer, Applied Scientist, Forward Deployed Engineer, MLOps/LLMOps Engineer, AI Platform Engineer, and AI Solutions Architect.

How to use it (per the README)

1. Start with the Common Questions Asked Across Companies — these recur everywhere.
2. Then go to your target company's section.
3. Answers are linked right below the question wherever they exist; more are being added over time.

Who maintains it Outcome School, an education group with an AI/ML program and content on YouTube, X/Twitter, LinkedIn, and GitHub. The README also points to a topic-wise companion repo: amitshekhariitbhu/ai-engineering-interview-questions.

What it deliberately does not do

• It doesn't promise every question has an answer (answers are linked "wherever we have them").
• It doesn't claim to be exhaustive or current — interviews change by team, level, and region, and the maintainers say they'll keep updating the list.