AI Engineering Interview Questions by Company

Key Takeaways#

• This resource is a public GitHub cheat sheet for AI engineering interviews.
• The repository organizes questions by company, including major AI labs and infrastructure companies.
• It is useful for builders who want practical interview prep across machine learning, systems, LLMs, agents, and product engineering.
• Treat it as a study map: verify each linked answer, practice explaining tradeoffs, and add your own notes before interviews.

What this resource is#

AI Engineering Interview Questions by Company is a public GitHub repository maintained by Pallavi Shekhar. The project describes itself as a cheat sheet for AI engineering interviews at top AI companies, with questions and answers organized company by company. The README includes sections for companies such as Anthropic, OpenAI, Google DeepMind, Meta, xAI, Mistral AI, Microsoft, Amazon, NVIDIA, Databricks, Hugging Face, Perplexity, and other AI-heavy employers.

That company-by-company structure is the main value. Most interview lists are grouped by topic only: machine learning basics, coding, system design, or behavioral questions. This repository lets candidates study the types of questions that appear around specific teams and brands. That is useful because an Anthropic-style safety conversation, a DeepMind research systems conversation, and a Databricks data platform conversation can test different instincts even when they all fit under the AI engineering label.

How to use it#

Start by choosing the companies you are targeting. Read the question list once without answering anything, then group the questions into themes: model fundamentals, data pipelines, LLM application design, evaluation, deployment, product judgment, and debugging. After that, write short answers in your own words. If an answer is linked in the repository, compare it with your draft and note what you missed.

For technical questions, practice aloud. A strong answer usually explains assumptions, gives a simple baseline, then describes how you would improve it under scale, latency, budget, safety, and reliability constraints. For example, an LLM application question is not only about choosing a model. It may require retrieval design, prompt format, evaluation data, guardrails, fallback behavior, and monitoring after launch.

Who should read it#

This resource is best for software engineers moving into AI product work, machine learning engineers preparing for applied AI roles, and students who want a clearer picture of what AI companies ask. It is also useful for founders and hiring managers because it shows the spread of questions candidates are studying.

It is not a substitute for building projects. Use the questions to find gaps, then build small demos that answer those gaps: a retrieval system, an eval harness, an agent workflow, a fine-tuning experiment, or a model-routing prototype. Interview prep gets much easier when each answer points back to something you have built and debugged.

Study plan#

1. Pick five target companies from the README.
2. Copy ten questions per company into a notes file.
3. Write a two-minute answer for each question.
4. Add one concrete project, metric, or production lesson to every answer.
5. Rehearse with a timer and force yourself to state tradeoffs instead of reciting definitions.

Source#

The source repository is available at pallavi-shekhar/ai-engineering-interview-questions-company-wise. OpenTools classifies it as a resource because it is a Markdown interview-prep collection, not an installable tool, AI model, MCP server, or organization.