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?
Full 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?
About This Question
This is a reported interview question from a uniswap interview during the onsite round.
About Uniswap Interview Reports
This question was reported by a candidate who interviewed at Uniswap. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.
Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at Uniswap are the higher-signal extractions to take from this report.
For broader preparation context, the Uniswap interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.
How To Practice This Type of Question
Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.
Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Uniswap reports consistently are the ones worth investing in; one-off niche problems are not.
During Your Uniswap Round
Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.
The single most predictive failure mode in Uniswap reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.