Citadel Coding Interview Questions
25 Citadel coding interview problems with full optimal solutions — 8 easy, 12 medium, 5 hard. Every problem ships with multiple approaches (brute-force first, then the optimal), complexity tables for each, company-specific tips on what an Citadel interviewer values, and a FAQ section.
Showing 8 problems of 25
- #1easyvery frequently asked
1. Two Sum
Citadel uses Two Sum as a calibration problem in SWE phone screens — they want to see if you immediately reach for a hash map rather than nested loops. At Citadel's latency targets, the difference between O(n) and O(n²) at 10M ticks/sec is not academic.
- #4hardoccasionally asked
4. Median of Two Sorted Arrays
This is perhaps the most technically demanding standard LeetCode hard — O(log(min(m,n))) requires a binary search on partition points, not on values. Citadel asks it to separate candidates who understand binary search at a deep level from those who merely know it as a lookup pattern. Precise handling of odd/even total length and boundary conditions is non-negotiable.
- #66easyoccasionally asked
66. Plus One
Plus One probes your carry-propagation instinct — the same mechanics underpin arbitrary-precision arithmetic libraries used in financial systems for exact decimal calculations. Citadel interviewers watch whether you handle the all-nines edge case (where a new leading digit is needed) cleanly and without special-casing.
- #121easyvery frequently asked
121. Best Time to Buy and Sell Stock
This problem is more at home at Citadel than anywhere else — it's literally what the firm does. Interviewers use it to see whether you recognize the 'running minimum' pattern and can articulate why a single-pass scan is sufficient. Expect follow-ups on multiple transactions and transaction costs.
- #146mediumvery frequently asked
146. LRU Cache
LRU Cache is directly relevant to Citadel's systems work: caching market data, reference data (security master), and computed risk metrics all require eviction policies. Citadel interviewers use this to verify you can compose a hash map and doubly-linked list into O(1) get and put — and that you understand why each structure is necessary.
- #239hardvery frequently asked
239. Sliding Window Maximum
Sliding Window Maximum is practically the interview problem for Citadel's trading systems domain — computing the rolling maximum price, maximum volume, or maximum order size over a fixed window is a core operation in market microstructure analytics. The monotonic deque solution is the expected answer; a sorted structure or brute force is insufficient.
- #295mediumvery frequently asked
295. Find Median from Data Stream
Computing a running median over a live data stream is a real operational metric at Citadel — median latency of order acknowledgements, median fill rate over a rolling window, median bid-ask spread. The two-heap technique is the standard interview answer, but Citadel pushes hard on the rebalancing logic and on what happens at O(log n) vs O(n).
- #1235hardoccasionally asked
1235. Maximum Profit in Job Scheduling
This problem is a weighted interval scheduling problem — find the maximum-profit subset of non-overlapping intervals. At Citadel, structurally identical problems appear in trade-off analysis: selecting the highest-value set of non-overlapping trading windows subject to latency budgets. It demands DP on sorted intervals with binary search, testing both algorithmic depth and implementation rigor.