Uniswap Interview Questions (2026)
1 questions · InterviewDB (1)
NFT Borrowing Service - Design a Lending Protocol for Digital Assets
Question Details
Round 1 Coding / OOD
Problem
Design a simplified NFT borrowing service. Users can list their NFTs for lending at a daily fee. Borrowers can rent an NFT for a fixed number of days and return it early. Implement the following interface:
python
class NFTBorrowingService:
def list_nft(self, nft_id: str, owner: str, daily_fee: float) -> bool:
pass
def borrow(self, nft_id: str, borrower: str, days: int, current_day: int) -> bool:
pass
def return_nft(self, nft_id: str, borrower: str, current_day: int) -> float:
**Returns** total fee charged (prorated to actual days held)
pass
def available_nfts(self) -> list:
**Returns** list of nft_ids currently available to borrow
pass
Example
service.list_nft("ape_001", "alice", 5.0) # listed at $5/day
service.borrow("ape_001", "bob", 10, day=0) # borrow for 10 days
service.available_nfts() # -> [] (ape_001 is out)
service.return_nft("ape_001", "bob", day=3) # returned early -> fee = 3 * 5.0 = 15.0
service.available_nfts() # -> ["ape_001"]
Follow-ups
- How do you handle an overdue return — should fees accumulate past the agreed days?
- What if the owner wants to cancel the listing while it is actively borrowed?
- How would you add a collateral system to protect lenders against non-returns?
- How would you redesign storage and lookups if the service scales to millions of NFTs?
Topics
Related companies
Uniswap Interview Process Overview
The Uniswap interview process typically includes a recruiter screen, one to two technical phone screens, and a 4-6 round on-site or virtual on-site loop. Each round serves a distinct calibration purpose: coding rounds measure correctness, code quality, and complexity reasoning; system design rounds measure architectural judgment at the appropriate level; behavioral rounds measure ownership, leadership scope, and collaboration. Reports tagged on LeakCode from 2024-2026 show Uniswap runs a calibrated process consistent with industry norms for companies of its tier.
Difficulty calibration: Uniswap coding rounds typically run medium difficulty with follow-up depth as the senior discriminator. System design rounds expect production-grade trade-off articulation at L4+ levels. Behavioral rounds expect quantified outcomes ("reduced p99 latency from 800ms to 120ms") rather than vague impact claims. The candidates who advance consistently demonstrate clear thinking out loud rather than perfect final answers.
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Real candidate-reported interview questions are a calibration tool, not a memorization target. Uniswap updates its question pool every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage approach: identify the patterns that appear repeatedly in Uniswap reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions above by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of Uniswap's pool. Reports tagged with quantified difficulty and explicit round type are higher-signal than reports without those tags. The metadata filters help you build a focused study plan in 1-2 hours rather than 8-10 hours of unstructured browsing.
Common Uniswap Interview Mistakes
Reports tagged "no hire" at Uniswap consistently surface a few patterns: jumping into code without clarifying requirements, coding silently for extended periods, missing edge cases (empty input, single element, large input, overflow), producing working code the candidate cannot refactor when probed, and behavioral stories that use "we" instead of "I" diluting individual signal. Strong candidates explicitly avoid these patterns by following a consistent round template.
The single most predictive failure mode in recent reports: not asking clarifying questions. Interviewers are explicitly trained to weight this dimension. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into implementation immediately. Strong candidates also verbalize their approach before writing code; weak candidates code in silence and lose the communication dimension of the round's calibration.