Citadel·Software Engineer·Technical Phone Screen·Junior

JuniorRejected

Jul 2026

Summary

Went into a Citadel SWE intern round 1 completely cold, no leetcode prep, and got hit with a stateful data structure and OOD problem that had an optimization twist. Did not finish. Interviewer was half-checked out the whole time and I'm still thinking about the money I probably left on the table.

Questions Asked (1)

Design and implement a custom stateful data structure with object-oriented principles and a greedy optimization component, adapting your approach as new constraints are introduced mid-problem.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I picked an approach early and the interviewer dropped new information on me, so I had to pivot.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a high-level design using appropriate data structures and OOP principles. Implement the core functionality with a greedy optimization component, and iteratively adapt your design as new constraints are introduced, explaining trade-offs at each step.

Pro tip: Demonstrate adaptability by explicitly stating assumptions and asking clarifying questions before diving into code; this shows you can handle ambiguity and evolving requirements, which is crucial in trading systems.

1. Clarify Requirements and Constraints

Ask questions to understand the problem scope, expected operations, performance requirements, and any initial constraints. Confirm the greedy optimization objective.

2. Design the Data Structure

Outline the core data structure using OOP principles, defining classes, interfaces, and relationships. Explain how it supports required operations efficiently.

3. Incorporate Greedy Optimization

Describe how a greedy algorithm will be integrated to optimize a specific metric (e.g., latency, throughput, memory). Justify why greedy is appropriate and discuss potential pitfalls.

4. Implement and Test

Write clean, modular code for the data structure and greedy component. Walk through test cases, including edge cases, to validate correctness and performance.

5. Adapt to New Constraints

As new constraints are introduced, analyze their impact, propose modifications to the design or algorithm, and explain trade-offs. Iterate as needed.

Key Points to Mention

Object-oriented design principles: encapsulation, inheritance, polymorphism, and composition.
Choice of data structures (e.g., heaps, trees, hash maps) and their time/space complexity.
Greedy algorithm design: local optimal choice, proof of correctness (e.g., exchange argument), and limitations.
Trade-offs between different approaches: time vs. space, simplicity vs. performance, and scalability.
Adaptability: how to refactor or extend the design when constraints change.
Testing strategies: unit tests, edge cases, and performance benchmarks.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.