Interview Guides
Everything we know about interviewing at Facebook: the process stage by stage, what each round tests, compensation by level, and reports from candidates who interviewed.
What the process looks like, and what Facebook is really testing for.
You can expect a structured loop that starts with recruiter screening, then moves into technical evaluations, and ends with behavioral and leadership-style conversations. Across the reports, the technical part often feels LeetCode-style for coding, then shifts toward system design or product-style design and deeper technical discussion.
What gets tested is a mix of problem solving, analytical thinking, and communication. The extracted topic data is dominated by SQL (percentile 100), Python (93), ML system design (98), data structures and algorithms (data structures 93, algorithms 89, problem solving 83), and system design (91) with strong emphasis on scalability (77). Behavioral interviewing (64), stakeholder management (58), cross-functional collaboration (68), and cross-functional back-to-back interviews (1 role reported) show up as well.
Timeline-wise, you should plan for a few weeks end to end. One report describes about four weeks end to end, and another describes a quick move where, after clearing early technical steps, later rounds came about a week later. Candidate reports also repeatedly highlight that interviewers care about how you interpret ambiguous prompts, communicate clearly while iterating, and handle follow-up questions, not just arriving at a final answer.
SQL and Python are not just present, they are top-priority topics here, and the process also includes ML system design (percentile 98), so you should prepare for both classic data work and architecture-level reasoning, and practice explaining your thinking under time pressure.
6 stages, based on 500 candidate reports.
You start with a recruiter contact to discuss your background and confirm fit for the role. In many reports, this is followed quickly by a technical step, and the recruiter is mainly about role alignment and interest.
You get an initial technical interview or technical screening that evaluates your technical depth. Reports describe LeetCode-style timed coding questions and, in some cases, a screen split across SQL and Python where you must produce code that passes test cases.
You discuss past experiences and how you align with Facebook's values and culture. Reports describe behavioral rounds that include ownership-style themes and leadership scenarios, and the topic set also includes stakeholder management and cross-functional collaboration.
You may go through technical assessments and multiple technical interviews. Reports describe additional coding rounds, algorithmic questions, and follow-ups that require clean communication and iteration, plus attention to prompt interpretation under pressure.
You encounter system design style interviews that test architecture-level reasoning. The extracted topics strongly emphasize system design (91), scalability (77), and ML system design (98), and reports also mention product-style design conversations tied to data modeling and metrics extraction.
You finish with a final loop that may include multiple team members and open-ended scenarios, plus a conversation with the hiring manager. Some reports also describe cross-functional back-to-back interviews to test collaboration and influence.
How often each skill shows up across reported interview loops.
Each guide has the questions Facebook interviewers actually ask, the loop structure, and total compensation by level.
Estimated total compensation: base salary plus stock and annual cash bonus.
Patterns from candidates who got offers, and the mistakes that most often sink a loop.
Read what candidates said about interviewing at Facebook: the loop, difficulty, and outcomes, straight from recent reports for each role.
Answered from real candidate and workplace data, marked up for rich results.
Verbatim snippets pulled from employee and candidate reviews.
Management should actively listen to employee feedback to improve the workplace.
The company was great when I joined, but it has since lost its appeal.
The team consists of some of the smartest people in the industry, making collaboration intellectually rewarding.
Smart colleagues but the work-life balance is lacking.
Candidates should be prepared for a demanding environment that prioritizes performance over work-life balance.
The intense performance reviews and laptop surveillance contribute to a poor work-life balance.
Companies that hire for the same data roles, with the same depth of prep.
