A reported Citi AI Engineer screen was a one-hour Karat interview with brief Python questions followed by two coding problems, including a directed-graph pickup assignment.
$130K
Avg. Base Comp
$190K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
One reported Citi AI Engineer interview was a one-hour Karat screen centered on Python and coding. The candidate said the first 10 minutes covered basic Python questions, followed by roughly 40 minutes of coding. They faced two coding problems, solved the first, and found the second substantially harder.
The detailed problem modeled travel on a directed graph: each location had one outgoing road, all paths led to a campground, and two cars collected people according to which car reached each location first. Practice turning that narrative into a clear graph representation, tracing arrival order from two starting points, and explaining how ties are handled. Under a limited coding window, candidates should clarify the inputs, state a workable traversal strategy, and leave enough time to attempt the harder second problem.
Only one candidate interview is described, so later stages and the full process length are not reported. Preparation should focus on the basic Python and directed-graph coding themes in that account, while remaining ready for a noticeable increase in difficulty between problems.
Synthesized from 1 candidate report by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Citi process.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Citi
Write a query to show the number of users, transactions, and total order amount per month in 2020
| Question | |
|---|---|
| Size of Joins | |
| Jars and Coins | |
| Hurdles In Data Projects | |
| Real-Time Transaction Streaming | |
| Concurrent LLM Serving | |
| Dijkstra implementation | |
| Replace Words with Stems | |
| String Palindromes | |
| Loan Model | |
| Seller Type Modeling | |
| Index Fund Return | |
| Client Solution Pushback | |
| International e-Commerce Warehouse | |
| Text Editor With OOP | |
| Logistic Regression from Scratch | |
| Unified Event Pipeline | |
| Data Cleaning Experiences | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Concentric Circles | |
| Optimizing Threshold Adjustment in Default Risk Models | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Employee Salaries | |
| Merge Sorted Lists | |
| Subscription Overlap | |
| Comments Histogram | |
| Closest SAT Scores |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reports that a one-hour Karat interview opened with about 10 minutes of basic Python questions. Review core Python syntax and be ready to explain concise choices aloud; no specific Python prompts were reported.
The candidate reports two coding questions in the following roughly 40 minutes and says they solved the first. Expect to clarify inputs and constraints, then communicate an implementable approach before moving into code.
One reported problem used a directed road graph in which every location had one outgoing road and paths ended at a campground. Two cars started separately and picked up people based on first arrival, with ties assignable to either car. Practice modeling traversal and comparing arrival order from two starts.