Reddit Interview

Updated July 12, 2026 · techinterview.org

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 — 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 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, 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 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 — 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 — 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 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):

• 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 — 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 — 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:

1. 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.
2. 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.
3. 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.
4. 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 — 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

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

1. Technical rigor: Harder than I expected for a “social media” company
2. Scale focus: Every question had a scale component
3. Python everywhere: They really care about Python skills
4. 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

1. Actually use Reddit: Browse different subreddits, notice patterns, have opinions
2. Focus on scale: Every answer should consider “what if 10M users?”
3. Know caching: Redis comes up a lot in system design
4. Write clean code: They care about code quality
5. Be pragmatic: They want builders, not perfectionists
6. 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.