Dataford · Comparison
AI Labs vs Big Tech: Inside the Interview
OpenAI and Google both want strong engineers — but they grade for different things. We compared the questions, difficulty, and candidate experience across the AI labs and Big Tech to see exactly how the two interviews diverge.
Summary · Key findings
AI labs interview for the model layer. Generative AI & LLMs make up 13% of AI-lab questions versus under 3% at Big Tech — a 4.6× gap and the single clearest divergence between the two. The labs want people who build with models, not just around them.
Big Tech still tests classic computer science. Coding and data-structures questions are 15% of the Big Tech loop, nearly double the labs'. The algorithmic screen remains the backbone of a Big Tech interview.
Analytical rigor is a Big Tech signature. A/B testing and statistics are a standard part of the Big Tech loop and almost absent at the labs. Experimentation discipline is something Big Tech still interviews for explicitly.
SQL belongs to Big Tech. SQL and data-manipulation questions are 5.4× more common in Big Tech loops. The warehouse-query muscle that Big Tech leans on heavily is something the labs test far less.
Same difficulty, very different experience. The labs are no harder than Big Tech (32% vs 29% of loops rated hard) but markedly less liked — 39% positive versus 60%. A fast-moving, still-maturing process leaves a different impression.
“Top AI company” has quietly split into two different things to prepare for. The interview at an AI lab and the interview at a Big Tech company are no longer the same exam.
We took the questions Dataford holds for the two clusters — OpenAI, Anthropic, Databricks, and NVIDIA on one side; Google, Meta, Amazon, Apple, and Microsoft on the other — and compared them by topic, as a share within each group. Then we layered in how hard each cluster's loops are rated and how candidates feel afterward.
The result is a clean before-you-apply map: where to spend your prep time depending on which side of this split you are aiming for. The full method is at the end.
The split
They grade for different things
Read this chart top to bottom and the philosophy of each side comes into focus. The labs front-load the model layer; Big Tech front-loads classic computer science and analytics.
Each bar is the topic's share within that group, so the two sides are comparable despite different totals.
The top two rows are the whole story in miniature. Generative AI and pipelines — building and feeding models — are where the labs spend their questions. Coding, SQL, A/B testing, and statistics — the fundamentals of a large product org — are where Big Tech spends its. Neither is wrong; they are interviewing for different jobs that happen to share a résumé.
Finding
Same difficulty, different feeling
It would be easy to assume the labs are simply harder. They are not, by much — about 32% of AI-lab loops are rated hard versus 29% at Big Tech. The real gap is in how the process feels.
The AI labs are growing fast and hiring against a moving bar, and the candidate experience shows it: less standardized, quicker, sometimes harder to read. That is worth knowing going in. A lab interview asks you to be comfortable with ambiguity in the process itself, not only in the problems.
Outlook
Which one are you preparing for?
Pick the side before you pick the prep plan. If you are targeting the labs, weight your time toward machine learning, LLMs, and the systems that train and serve models, and accept that the process will feel less scripted than you might like.
If you are targeting Big Tech, the older playbook still holds: classic coding, SQL, experimentation, and product judgment, run through a more standardized loop. The candidates who struggle most are usually the ones who prepared for the other side's exam.
Prep for the company in front of you
Company-specific interview guides for the AI labs and Big Tech — built from how each one actually tests.
Explore interview guidesFAQ
Frequently asked questions
How is an OpenAI or Anthropic interview different from a Google one?+
The topic mix diverges sharply. AI labs lean toward Generative AI and LLMs (13% of their questions vs under 3% at Big Tech) and data pipelines, while Big Tech leans on classic coding, SQL, A/B testing, and statistics. Both test engineering ability, but they grade for different specialties.
Are AI lab interviews harder than Big Tech?+
Not meaningfully. About 32% of AI-lab loops are rated difficult or very difficult, versus 29% at Big Tech — close. The bigger difference is the experience: AI-lab interviews are rated positive far less often (39% vs 60%), reflecting a faster, less standardized process.
Do I still need to grind LeetCode for AI labs?+
Less than for Big Tech, but not zero. Classic coding is 15% of the Big Tech loop versus about 8% at the labs. For AI labs, time spent on machine learning, LLMs, and systems for training and serving models generally pays off more than pure algorithms practice.
Which group tests SQL and experimentation?+
Big Tech, clearly. SQL is about 5.4× more common in Big Tech loops, and A/B testing and statistics are largely a Big Tech phenomenon. If you are interviewing at the labs, those topics carry much less weight.
Which companies are in each group?+
For this comparison, AI labs are OpenAI, Anthropic, Databricks, and NVIDIA; Big Tech is Google, Meta, Amazon, Apple, and Microsoft. The topic comparison uses questions tagged to those companies; difficulty and sentiment use their interview experiences.
Methodology
How this report was built
The topic comparison uses Dataford's published questions tagged to each group: AI labs (OpenAI, Anthropic, Databricks, NVIDIA) and Big Tech (Google, Meta, Amazon, Apple, Microsoft), 1,476 tagged questions in total. Each topic is reported as a share within its group, so the two sides are comparable even though Big Tech carries more tagged questions overall.
Difficulty and experience come from the interview-experience dataset, grouped by the same archetypes: the share of each cluster's loops rated difficult or very difficult, and the share rated a positive experience.
Company tagging coverage is uneven and these are representative firms, not the entire field; the comparison shows the shape of the difference, not a precise league table. Figures are current as of June 2026.