Meta AI Research Scientist interview typically runs 6 rounds: phone screen, 3 technical, 1 system design, and 1 behavioral. Timeline is about 2 days to several weeks, and the process is highly standardized.
$167K
Avg. Base Comp
$315K
Avg. Total Comp
5-6
Typical Rounds
3-6 weeks
Process Length
We’ve seen a consistent pattern in Meta’s AI Research Scientist interviews: the company cares less about whether you can recite the latest ML paper and more about whether you can build, reason, and defend under pressure. Multiple candidates reported that the coding bar felt closer to Meta’s standard engineering loop than a research-only screen, with LeetCode-style problems, complexity follow-ups, and even blank-editor implementation tasks like K-means from scratch. One candidate explicitly noted that there was less direct ML depth than expected, and another said the technical questions were pulled from Meta’s tagged pool rather than anything exotic. That tells us a lot about what Meta is optimizing for: strong problem-solving fundamentals, not just research fluency.
A recurring theme is that the ML portion is real, but often narrower and more applied than candidates expect. We’ve seen questions around binary classification system design, train/test click-through distribution mismatch, multimodal LLMs, and architecture-level understanding like LLaMA — yet the overall tone stayed structured and practical rather than deeply academic. The non-obvious make-or-break factor is how well candidates can connect their research background to concrete product or system tradeoffs without losing speed on coding. Several candidates described the process as fair and professional, but also repetitive and standardized, which means polished, concise explanations matter. If your answers sound overly theoretical or you struggle to justify complexity choices out loud, that’s where Meta seems to separate strong researchers from strong hires.
Synthetized from 5 candidates reports by our editorial team.
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Real interview reports from people who went through the Meta process.
Outcome: Not specified Format: Virtual (assumed) Interview Type: Phone screen + 4 rounds (3 technical, 1 system design, 1 behavioral) Year: 2024
I went through Meta's Research Scientist interview process in 2024. It started with a phone screening, then moved into 4 rounds: 3 technical, 1 system design, and 1 behavioral.
Technical Rounds
The technical questions were LeetCode-style problems pulled from Meta's tagged questions. Nothing exotic — if you go through Meta's commonly tagged LeetCode problems, you're preparing the right way.
System Design Round
Pretty standard system design problem. Nothing too out of the ordinary — par for the course.
Behavioral Round
Also standard. Nothing unexpected. It followed the typical behavioral interview format you'd prepare for at any major tech company.
The biggest surprise was how little ML content there was — and this is for a Research Scientist position, which is an ML-focused role. I really wasn't expecting that going in. Focus heavily on LeetCode, specifically Meta-tagged problems, and don't over-index on ML prep. Make sure your system design and behavioral answers are solid too, since those rounds are very much part of the equation.
Prep tip from this candidate
Focus your prep on Meta-tagged LeetCode problems for the technical rounds, and don't over-index on ML content — despite this being a Research Scientist role, ML was surprisingly minimal in the actual interview.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Meta
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Experiment Validity | |
| Merge Sorted Lists | |
| Scrambled Tickets | |
| Friendship Timeline | |
| P-value to a Layman | |
| Swipe Precision | |
| Nearest Common Ancestor | |
| Using R Squared | |
| Bank Fraud Model | |
| Recurring Character | |
| Hurdles In Data Projects | |
| Radix Addition | |
| Network Experiment Design | |
| Target Indices | |
| Twenty Variants | |
| Find Bigrams | |
| Replace Words with Stems | |
| One Element Removed | |
| Fill None Values | |
| Booking Regression | |
| Changing Composer | |
| Swiping App Design | |
| Detecting Firearm Sales | |
| Good Grades and Favorite Colors | |
| Level Of Rain Water In 2D Terrain | |
| Matrix Rotation | |
| Append Frequency | |
| Greatest Common Denominator | |
| Random Forest Explanation |
Synthesized from candidate reports. Individual experiences may vary.
The process typically begins with a recruiter reach-out followed by a phone screen. This stage often includes 1-2 LeetCode-style coding questions at an easy-to-medium level, and in some cases serves as the first filter before the full loop.
Candidates usually complete multiple coding rounds focused on Meta-style LeetCode problems. The questions can range from medium to hard and may include arrays, merges, topological sorting, and other standard algorithmic patterns, with interviewers often probing complexity and alternative solutions.
One round is more research-oriented and may cover past research projects, ML/NLP topics, and applied ML reasoning. Candidates have reported questions on multimodal LLMs, LLaMA, learning rate choices, binary classification system design, and train/test distribution mismatch.
The loop includes a standard system design round, sometimes framed as an ML system design problem. The discussion is generally described as conventional rather than highly specialized, with emphasis on structuring a practical design and explaining tradeoffs.
The final round is a behavioral interview covering background, collaboration, and how candidates handle ambiguity. Questions are usually straightforward and may include discussion of a proud project, PhD challenges, and prior work experience.