# Reddit Interview Source: https://www.techinterview.org/companies/reddit-interview/ Updated: 2026-07-12 · techinterview.org **TL;DR —** Reddit's software engineering interview runs a standard big-tech loop: a recruiter screen, a technical phone screen, and a virtual onsite covering coding, system design, and behavioral rounds. Coding rounds focus on data structures and algorithms, while system design carries more weight for senior candidates and often maps to Reddit-scale problems like feeds, ranking, and comment threads. Behavioral rounds probe collaboration, ownership, and how you work through ambiguity. ## Reddit Interview Process: Complete 2026 Guide Interviewed at Reddit in early 2024 for a backend engineer role. The process was surprisingly rigorous for a company of their size. Here's everything you need to know. ### Overview Reddit is at an interesting phase - post-IPO, scaling rapidly, but still maintaining that startup-ish culture. The interview reflects this: expect FAANG-level technical rigor but with more emphasis on practical engineering and less on obscure algorithms. They care a lot about handling scale - Reddit gets billions of pageviews monthly - and about building features that millions of users actually love. ### Interview Structure **Initial Screen (30 minutes):** - Recruiter call to discuss background — a high-level walk through your resume; keep it to a tight summary of your most relevant recent work and what you want next. - Talk about why Reddit — have a specific answer beyond "I use it daily," ideally naming a product area or scale problem you'd want to own. - [Salary expectations](/post/3233474669/salary-negotiation-2026/) — give a range rather than a single number, and be ready for the recruiter to press for a figure early. - Timeline and logistics — expect questions about competing processes and start-date flexibility; a live competing offer can speed the loop up. **Technical Phone Screen (45-60 minutes):** - 1-2 coding problems — often a single medium if the discussion runs long. - Live coding in CoderPad — no autocomplete or compiler, so practice writing runnable code in a plain editor while narrating your approach. - Medium [leetcode](/problem-index/) difficulty — arrays, strings, and hash maps show up most; they expect the optimal answer, not just one that passes. - Some discussion about your experience — the interviewer often ties the problem back to something on your resume, so connect it explicitly. My phone screen: Implement a [rate limiter](/post/3233474159/system-design-rate-limiter-token-bucket-sliding-window-leaky-bucket-distributed-rate-limiting-api-gateway/), then discuss how I'd deploy it at Reddit scale. Good indicator of their focus. **Virtual Onsite (4 hours):** - 2 coding rounds (45 min each) — one leans data-structure heavy, the other more algorithmic; both expect a working, optimal solution with time to spare. - 1 [system design](/category/system-design/) round (60 min) — the round that most often decides the outcome, so treat it as the main event. - 1 behavioral/culture fit round (30 min) — shorter, but they weigh product judgment and collaboration heavily. - 15 min break between rounds ### Technical Focus Areas **1. Data Structures & Algorithms (Core)** Medium to hard leetcode: - Trees and graphs (BFS, DFS) — the most common category; be fluent in level-order traversal, cycle detection, and shortest paths, since comment threads and follow graphs map directly onto these. - Hash tables and sets — reach for these to turn an O(n²) scan into O(n); interviewers watch for whether you spot the lookup that removes a nested loop. - String manipulation — parsing, frequency counts, and in-place edits; know how to avoid building throwaway copies on large inputs. - Some DP (not super heavy) — expect one classic like longest-substring or coin-change rather than an obscure multi-dimensional problem; get the recurrence and base case right. - [Sliding window, two pointers](/post/3233474160/coding-interview-two-pointers-sliding-window-patterns-array-string-problems-fast-slow-pointer-variable-window/) — the go-to for subarray and substring questions; practice the variable-size window and the fast/slow pointer variants. They want efficient solutions. Brute force won't cut it. **2. System Design (Very Important)** Expect Reddit-scale problems: - Design a voting system (upvotes/downvotes) — the signature Reddit question; be ready to talk about write amplification, hot posts, and keeping score counts fast to read. - Design a comment tree structure — focus on storing and fetching deeply nested threads efficiently, plus pagination of very large threads. - Design a [feed generation system](/post/3233474168/system-design-twitter-news-feed-timeline-fanout-on-write-fanout-on-read-celebrity-problem-ranking-caching/) — discuss fan-out on write versus read and how ranking and freshness interact at scale. - Design a notification service — cover delivery guarantees, batching, and dedup so a busy thread doesn't flood a user. - Caching strategies at massive scale — expect follow-ups on invalidation, hot keys, and what happens during a cache-miss storm. Focus on: - Handling millions of concurrent users — quantify it; put real numbers on QPS and storage before you start drawing boxes. - Data consistency vs availability tradeoffs — say where you'll accept eventual consistency (vote counts) and where you won't (a user's own action). - Caching strategies (Redis heavily used at Reddit) — name the pattern (cache-aside, write-through) and the eviction and TTL choices behind it. - [Database sharding](/post/3233459955/database-sharding/) and replication — pick a shard key, explain how it avoids hotspots, and cover what happens when a shard fails. **3. Python/Backend Skills** Reddit is heavily Python-based (though they're adding more [Go](/post/3233474456/go-golang-interview-questions-2025-goroutines-channels-interfaces-error-handling-context-generics-concurrency-patterns/)): - Strong Python knowledge expected — they probe idioms, the standard library, and how you reason about the GIL when a task is CPU-bound versus I/O-bound. - Web frameworks (Flask/Django) — know the request lifecycle, middleware, and the ORM well enough to explain where a slow query comes from. - REST API design — versioning, pagination, idempotency, and sensible status codes; be ready to sketch an endpoint contract. - [Database optimization](/post/3233461821/database-indexing-interview-guide/) — reading query plans, adding the right index, and killing N+1 queries are the specifics they look for. - Caching patterns — where to cache, how to invalidate, and how to keep cache and database from drifting apart. ### Coding Interview Details **Round 1 - Data Structures:** Problem I got: "Implement a comment tree where you can efficiently fetch all child comments of a given comment." They wanted: - Tree traversal algorithm — DFS to gather a subtree, with a clear choice between recursion and an explicit stack for deep trees. - Discussion of [time/space complexity](/big-o-cheat-sheet/) — state it for both the fetch and the storage, and note how it changes as the tree deepens. - How to optimize for Reddit's use case (millions of comments) — pagination, lazy loading of deep replies, and precomputed paths so you don't walk the whole tree. - Database schema design — how you'd model parent/child (adjacency list vs materialized path) and index it for fast child lookups. **Round 2 - Algorithms:** Problem: "Given user voting history, detect vote manipulation (bots)." Required: - Pattern detection algorithms — grouping by account, timing, and target to surface coordinated behavior rather than judging votes in isolation. - Statistical analysis — baselines and thresholds; be ready to justify why a given rate looks anomalous. - Handling large datasets efficiently — streaming or windowed processing so you never load the full voting history into memory. - Practical tradeoffs (false positives vs false negatives) — explain the cost of flagging a real user versus missing a bot, and where you'd set the line. This is typical Reddit - real problems they actually face. ### System Design Interview Question: "Design Reddit's voting system to handle 10 million votes per minute." Key areas to cover: - **Data Model:** How to store votes — one row per (user, post) so you can enforce one vote each and flip it cheaply. - Denormalization for performance — keep a running score on the post so reads don't aggregate the vote table every time. - Score calculation — decide whether the ranking score is computed on write or by a background job, and how often it refreshes. - **Scale:** Database sharding strategy — shard by post or subreddit so a viral thread's votes land together and stay balanced. - Caching layer (Redis) — serve hot scores from Redis and reconcile with the database asynchronously. - Queue for async processing — absorb vote spikes into a queue so the write path stays fast under load. - **Consistency:** Eventual consistency acceptable? — usually yes for the displayed count; a few seconds of lag on a score is fine. - How to handle conflicts — last-write-wins on a single user's vote, since there's only one authoritative row per user. - Vote fraud prevention — rate limits, account-age checks, and offline anomaly scoring rather than blocking on the write path. - **Performance:** Read vs write optimization — reads vastly outnumber writes, so optimize the read path first and batch the writes. - Caching strategies — cache rendered listings and hot scores, and plan invalidation for when a score changes. - CDN usage — push static assets and cacheable pages to the edge to keep origin load down. The interviewer pushed hard on specifics - "How exactly would you shard? What happens if a shard goes down? How do you ensure vote counts are accurate?" ### Behavioral Interview Reddit has a strong engineering culture. They look for: - **Passion for the product:** Actually use Reddit, have opinions - **User focus:** Care about the community - **Pragmatism:** Ship features, don't overengineer - **Collaboration:** Work well with product/design Questions I got: - "Tell me about a time you disagreed with a product decision." - "How do you handle technical debt?" - "Describe a feature you shipped that users loved." - "What would you improve about Reddit?" That last one is important - have real, thoughtful suggestions ready. ### Preparation Strategy **For Coding (3-4 weeks):** - 100+ leetcode problems (focus on medium) - Emphasize trees, graphs, hash tables - Practice live coding - they'll watch you think - Write clean, commented code **For System Design (2-3 weeks):** - Study Reddit's architecture (tech talks, blog posts) — note how and why they combine Redis, relational stores, and queues. - Understand caching patterns deeply — cache-aside vs write-through, TTL choices, and invalidation are where these rounds probe hardest. - Learn about database sharding — shard-key selection, rebalancing, and the cost of cross-shard queries. - Practice designing social features — voting, feeds, comments, and notifications, since your prompt will be one of these. **For Behavioral (1 week):** - Use Reddit daily, note what works/doesn't — keep a running list of concrete product observations to pull from. - Prepare [STAR stories](/post/3233460379/behavioral-interview-questions-2026-star-method-amazon-leadership-principles-and-winning-answers/) — three or four flexible ones covering conflict, a shipped feature, and a failure you learned from. - Think about scale problems — tie your stories to real user or traffic numbers where you can. - Have thoughtful product opinions — especially a specific answer to "what would you improve about Reddit?" ### Difficulty: 7.5/10 Comparable to mid-tier FAANG. Easier than Google/Meta (9/10), harder than most Series B startups (6/10). The coding is standard leetcode medium. The system design is where they really test you - expect to go deep on scale and caching. ### Compensation (2024 data) - **New grad:** $140-160K base + $40-60K stock - **Mid-level (3-5 YOE):** $160-200K base + $60-100K stock - **Senior (5-8 YOE):** $200-260K base + $100-200K stock - **Staff+:** $280-400K+ [total comp](/total-comp-calculator/) Stock vests over 4 years. 10-15% annual bonus. Post-IPO, stock is liquid. ### Culture & Work Environment **Pros:** - Smart, passionate engineers - Interesting technical challenges at scale - Product people actually care about users - Remote-friendly (really!) - Good work-life balance (45 hours/week typical) **Cons:** - Lots of legacy code (site is 18+ years old) - Some tech debt - Not as much $$ as FAANG - Post-IPO pressure to grow revenue ### Things That Surprised Me - **Technical rigor:** Harder than I expected for a "social media" company - **Scale focus:** Every question had a scale component - **Python everywhere:** They really care about Python skills - **Product involvement:** Engineers have strong product opinions ### Red Flags to Watch - Ask about on-call rotation (can be heavy for some teams) - Ask about technical debt (varies by team) - Ask about team stability (some teams have higher turnover) - Ask about roadmap (post-IPO priorities shifting) ### My Experience Did well on coding rounds - solved both problems optimally with clean code. System design was challenging but I covered the main areas. Behavioral went great - I'm an active Reddit user so had genuine enthusiasm. Got the offer but ended up going elsewhere for more money. Would've been happy at Reddit though - seemed like a good place to work on real scale problems. ### Tips for Success - **Actually use Reddit:** Browse different subreddits, notice patterns, have opinions - **Focus on scale:** Every answer should consider "what if 10M users?" - **Know caching:** Redis comes up a lot in system design - **Write clean code:** They care about code quality - **Be pragmatic:** They want builders, not perfectionists - **Ask good questions:** About team, tech stack, roadmap ### Resources That Helped - Reddit Engineering Blog (redditblog.com) - System Design Primer (GitHub) - Grokking the System Design Interview - Leetcode premium (for Reddit-specific questions) - Redis documentation (seriously, know Redis) Reddit is a solid choice if you want to work on real scale problems, care about community, and want better work-life balance than FAANG. The interview is tough but fair - prepare well and you'll do fine.