Interview Atlas · Company guides
What each employer visibly values.
19 guides across 4groups. Each one is written from the company's own public material — engineering writing, research pages, published job descriptions, and public talks — and lists those sources so you can check the claims yourself.
These are preparation guides, not leaked loops. Where no public source describes a stage, we do not invent one. Read a guide alongside the process guide — the stages are largely common across the industry; what differs is what each company rewards inside them.
Frontier labs
OpenAI
A frontier lab whose research reaches most engineers as a product and an API they already build against.
- AI Engineer
- LLM Engineer
- Machine Learning Engineer
- +4 more
5 cited sources
Anthropic
A lab that publishes its interpretability and safety work in detail, and expects candidates to have read some of it.
- AI Research Scientist
- AI Engineer
- LLM Engineer
- +3 more
7 cited sources
Google DeepMind
A publishing research organisation with a reinforcement learning heritage, now also shipping Gemini inside Google.
- AI Research Scientist
- Machine Learning Engineer
- LLM Engineer
- +3 more
6 cited sources
Meta AI
The lab whose main external output is code and open weights, sitting on top of hyperscale product infrastructure.
- Machine Learning Engineer
- AI Engineer
- LLM Engineer
- +4 more
7 cited sources
xAI
A smaller, faster-moving frontier lab that ships Grok into an API and directly into X.
- AI Engineer
- LLM Engineer
- Machine Learning Engineer
- +3 more
5 cited sources
AI infrastructure
NVIDIA
The GPUs, interconnect and CUDA software stack that most production AI actually runs on.
- Machine Learning Engineer
- LLM Engineer
- AI Solutions Architect
- +3 more
6 cited sources
Databricks
The lakehouse platform where Spark, Delta Lake and MLflow carry other companies' production data and model workloads.
- Data Engineer (AI-focused)
- Machine Learning Engineer
- MLOps Engineer
- +2 more
7 cited sources
Scale AI
Data labelling, human feedback and model evaluation run as an industrial operation for other people's models.
- Machine Learning Engineer
- AI Engineer
- Data Engineer (AI-focused)
- +3 more
6 cited sources
Hugging Face
The open-source libraries and model hub most of the ecosystem builds on and publishes to.
- Machine Learning Engineer
- AI Engineer
- LLM Engineer
- +3 more
7 cited sources
AI-native products
Perplexity
A consumer answer engine where the hard problem is retrieval quality and citations that survive a click.
- AI Engineer
- LLM Engineer
- Machine Learning Engineer
- +4 more
3 cited sources
Cursor
An AI code editor whose users are engineers who spot a wrong suggestion instantly and remember it.
- AI Engineer
- LLM Engineer
- Machine Learning Engineer
- +4 more
5 cited sources
Mistral AI
A European lab with open-weight releases and a paid platform, sitting between research and product.
- AI Research Scientist
- LLM Engineer
- Machine Learning Engineer
- +3 more
5 cited sources
Cohere
Enterprise language models built to run inside a customer's own security boundary, not just an API call away.
- AI Engineer
- NLP Engineer
- Machine Learning Engineer
- +4 more
6 cited sources
Runway
Generative video and image tools where cost per generation and iteration speed are the product.
- AI Research Scientist
- Machine Learning Engineer
- AI Engineer
- +3 more
5 cited sources
Big tech AI
Amazon
AWS AI services, Alexa and a large applied-science org, with the Leadership Principles running through every interview.
- Machine Learning Engineer
- AI Engineer
- MLOps Engineer
- +3 more
6 cited sources
Microsoft
Azure AI infrastructure, a long-running research division, and the Copilot product surface built on the OpenAI partnership.
- AI Engineer
- Machine Learning Engineer
- AI Solutions Architect
- +3 more
6 cited sources
Apple
On-device and privacy-preserving machine learning, inside a company that tells candidates remarkably little.
- Machine Learning Engineer
- AI Engineer
- AI Research Scientist
- +2 more
6 cited sources
Netflix
Recommendation and personalisation at enormous scale, under an unusually explicit and demanding published culture.
- Machine Learning Engineer
- AI Engineer
- Data Engineer (AI-focused)
- +2 more
6 cited sources
Uber
Real-time marketplace machine learning — pricing, ETA, matching and forecasting — with heavy operational demands.
- Machine Learning Engineer
- MLOps Engineer
- Data Engineer (AI-focused)
- +2 more
4 cited sources
Know the company. Now practise the role.
A company guide tells you what to emphasise. It does not rehearse you. Once you know which employer and which title you are aiming at, work through the role bank — the skills that job is hired for and the questions its loop actually asks.