Real Interview Questions by Company [2026-2027]

Google has the deepest question coverage in LeakCode's database. The 1Point3Acres source is particularly rich for Google L4/L5 SWE reports — Chinese-language posts that are invisible to English-only research tools like LeetCode Discuss. Google's phone screen is consistently dominated by sliding window, two pointers, and hash map problems at medium difficulty.

The Google onsite coding rounds (4-5 sessions) skew toward harder graph, DP, and tree problems. System design rounds at Google are notably different from Meta/Amazon — interviewers expect Spanner/Bigtable-level distributed systems knowledge at senior levels. Behavioral rounds focus on the STAR method against leadership and ambiguity questions.

Top topics reported: {'audit_topic': 'graph', 'cnt': 333} · {'audit_topic': 'dynamic_programming', 'cnt': 171} · {'audit_topic': 'system_design', 'cnt': 153}

Meta's coding round is the most predictable of any FAANG company. The dominant topics are BFS/DFS, dynamic programming, and array manipulation — with a strong bias toward problems involving trees (the internal codebase is heavily graph-structured). Meta E5 candidates report that the "harder" coding round is almost always a graph or DP problem with a tight time constraint.

Meta's behavioral round uses a specific values framework. Candidates who do not prepare for Meta's cultural values questions perform significantly worse than technical performance would predict. LeakCode's behavioral question coverage for Meta is strong across both E4 and E5 levels.

Top topics reported: {'audit_topic': 'system_design', 'cnt': 103} · {'audit_topic': 'arrays', 'cnt': 75} · {'audit_topic': 'binary_tree', 'cnt': 46}

Amazon's OA pipeline is one of the most well-documented in LeakCode's database. The HireVue/Codility platform cycles through a set of ~30-40 problem templates for SDE I/II. Candidates who have seen these templates (available on LeakCode) have a significant advantage. The OA is two coding problems, 90 minutes — medium/medium-hard difficulty.

Amazon's onsite is distinctive: every interviewer is assigned a Leadership Principle to probe, making behavioral prep as important as technical prep. LeakCode has the strongest behavioral LP coverage of any source — the 1Point3Acres Chinese community has extensively documented LP patterns for L5 and L6 SDE positions.

Top topics reported: {'audit_topic': 'system_design', 'cnt': 517} · {'audit_topic': 'graph', 'cnt': 271} · {'audit_topic': 'dynamic_programming', 'cnt': 233}

Apple's interview process is more team-dependent than other FAANG companies — hiring managers have more autonomy, so question patterns vary by org (hardware, iOS, ML, services). That said, LeakCode's database shows consistent patterns: Apple coding rounds lean toward medium-difficulty problems with emphasis on correctness and code quality over speed.

Apple system design rounds are notably hardware-aware. Distributed systems questions at Apple often involve constraint reasoning about device-level and edge computing scenarios. Apple's behavioral rounds are less scripted than Amazon's LP approach — expect open-ended culture fit questions.

Top topics reported: {'audit_topic': 'system_design', 'cnt': 19} · {'audit_topic': 'strings', 'cnt': 11} · {'audit_topic': 'graph', 'cnt': 11}

Netflix's engineering interviews are senior-skewed — they hire almost exclusively at senior+ levels. The coding round emphasis is on Java/C++ performance-sensitive code and distributed systems awareness. Netflix is unusual in that it does not use LeetCode-style puzzle problems heavily; it prefers domain-relevant system problems.

Netflix's culture (Freedom and Responsibility) is a significant interview component. Expect probing questions about how you handle ambiguity, disagree with decisions, and manage without process. LeakCode has strong coverage of Netflix's culture-fit questions from Reddit and Blind.

Top topics reported: {'audit_topic': 'system_design', 'cnt': 26} · {'audit_topic': 'algorithms', 'cnt': 15} · {'audit_topic': 'system design', 'cnt': 9}

Microsoft's interview process is one of the most consistent across teams — the company standardized its interview format more aggressively than most. Coding questions trend toward medium difficulty with an emphasis on tree/graph traversal and dynamic programming. Microsoft's OA uses Codility for new grad and is heavily pattern-matched.

Microsoft's "As Appropriate" (AA) round — an informal final interview with a senior director — is relatively unique among big tech. Candidates who are not aware of this round are sometimes caught off guard. LeakCode has well-documented AA round reports from Reddit.

Top topics reported: {'audit_topic': 'system_design', 'cnt': 156} · {'audit_topic': 'arrays', 'cnt': 88} · {'audit_topic': 'strings', 'cnt': 76}

Stripe is known for its "bring your own language" coding format and a strong emphasis on code review and technical writing. The take-home component is a real differentiator — Stripe gives candidates a realistic engineering task rather than a LeetCode puzzle. LeakCode has strong coverage of Stripe's take-home prompt patterns.

Stripe's system design round is payments-domain-heavy. Expect questions about idempotency, exactly-once delivery, distributed transaction patterns, and API design for financial systems. Behavioral rounds at Stripe focus on ownership, bias toward action, and attention to detail.

Top topics reported: {'audit_topic': 'algorithms', 'cnt': 19} · {'audit_topic': 'system_design', 'cnt': 8} · {'audit_topic': 'strings', 'cnt': 5}

OpenAI's interview process has evolved rapidly as the company scaled from ~400 to 3,000+ employees. Current reports indicate a strong ML systems emphasis even for pure SWE roles — infrastructure engineers at OpenAI are expected to understand GPU memory hierarchies, distributed training, and large-scale inference serving.

Coding rounds at OpenAI are medium-to-hard with a preference for problems where the optimal solution requires insight rather than pattern memorization. Research engineer roles have a heavier ML theory component (transformers, attention mechanisms, optimization). LeakCode's OpenAI coverage is growing rapidly as headcount scales.

Top topics reported: {'audit_topic': 'system design', 'cnt': 22} · {'audit_topic': 'strings', 'cnt': 9} · {'audit_topic': 'graph', 'cnt': 7}

Anthropic is one of the hardest companies to get interview data from due to its selective size and NDA culture. LeakCode's coverage is smaller but growing — the most reliable signal comes from Reddit's r/MachineLearning and r/cscareerquestions, where candidates occasionally post de-identified reports.

Anthropic's SWE interview is known for a very high bar on systems thinking and safety-aware reasoning. The research track has a significant reading comprehension component — candidates report being asked to analyze and critique ML papers. Infrastructure roles focus on scalable ML serving and training pipeline architecture.

Top topics reported: {'audit_topic': 'hash_table', 'cnt': 3} · {'audit_topic': 'tokenization,nlp,ml,strings,trie,greedy,dynamic-programming,debugging', 'cnt': 1} · {'audit_topic': 'system_design', 'cnt': 1}

ByteDance has one of the strongest question signals in LeakCode's database from the 1Point3Acres source. The Chinese-language tech community has extensive ByteDance interview reports — the company recruits heavily from Chinese universities and the 1p3a community documents these rounds in detail.

ByteDance's coding rounds are notably harder than most FAANG at the same level — the company targets competitive programmers and the difficulty bar skews toward LeetCode hard. Data structure and algorithm knowledge is tested explicitly, including less-common structures like segment trees and monotonic stacks. TikTok US interviews follow similar patterns.

Top topics reported: {'audit_topic': 'graph', 'cnt': 24} · {'audit_topic': 'dynamic_programming', 'cnt': 22} · {'audit_topic': 'system_design', 'cnt': 13}

Uber's interview process reflects its mapping and real-time logistics domain. System design rounds frequently involve geo-spatial problems: how would you design the dispatch system, the surge pricing engine, or the ETA prediction service? These are domain-flavored distributed systems questions where knowing Uber's business model helps.

Coding rounds at Uber are medium difficulty with a trend toward graph and heap problems. The interview is standardized across most engineering orgs (unlike Apple). LeakCode's Uber coverage benefits from strong Reddit signal — Uber employees and candidates are active on r/cscareerquestions.

Top topics reported: {'audit_topic': 'dynamic_programming', 'cnt': 39} · {'audit_topic': 'arrays', 'cnt': 37} · {'audit_topic': 'design', 'cnt': 28}

Airbnb's interview process includes a distinctive "cross-functional collaboration" component that tests how you work with PMs and designers, not just other engineers. Candidates report this as a meaningful differentiator — purely technical candidates who do not prepare for the collaboration scenarios underperform.

Coding rounds at Airbnb trend toward array, string, and hash map problems. Airbnb has a strong product sense interview component for senior+ levels. System design rounds often involve marketplace or search-related problems. LeakCode's coverage is particularly strong from LeetCode Discuss where Airbnb interview reports are plentiful.

Top topics reported: {'audit_topic': 'algorithms', 'cnt': 49} · {'audit_topic': 'system_design', 'cnt': 19} · {'audit_topic': 'arrays', 'cnt': 4}

Roblox has grown rapidly in engineering headcount and has a growing LeakCode footprint. Its interview process is similar to tier-1 FAANG in structure: phone screen, OA, and multi-round onsite. The platform's real-time 3D engine and large-scale creator economy create distinctive system design angles.

Coding rounds at Roblox lean toward medium difficulty. Infrastructure and platform engineering roles have strong distributed systems components. Roblox is known for a positive candidate experience and transparent feedback. LeakCode's coverage comes primarily from Reddit and LeetCode Discuss.

Top topics reported: {'audit_topic': 'system_design', 'cnt': 11} · {'audit_topic': 'strings', 'cnt': 4} · {'audit_topic': 'hash_table', 'cnt': 4}