Upstart·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate

Jun 2026

Summary

Upstart's AI-coding round had me implement a vote-counting system while using AI tooling to write the code, then defend my design choices and drive the product discussion myself rather than deflecting questions back to the interviewer.

Questions Asked (1)

Implement a vote-counting system. Use AI tooling to write the code quickly, explain your approach, make product and design decisions proactively, and be ready to extend the solution based on follow-ups.

Algorithms & Data StructuresTechnical Trade-offsProduct Sense & Ideation

Author's notes

The AI tooling part threw me a bit because most coding rounds punish you for not writing everything by hand.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases (e.g., tie-breaking, vote validation, scale) before coding. Use AI to generate a clean, extensible implementation (e.g., a class with methods to cast and tally votes), then walk through your design decisions and trade-offs. Be prepared to extend the solution with follow-ups like real-time updates, weighted votes, or distributed counting.

Pro tip: Treat the AI as a pair programmer: narrate what you're prompting and why, and always review the generated code for correctness and edge cases. Proactively mention how you'd test and monitor the system in production.

1. Clarify Requirements and Constraints

Ask questions to understand the scope: number of candidates, expected vote volume, need for real-time results, persistence, and security. Define what constitutes a valid vote and how ties are handled.

2. Design the Data Model and API

Choose data structures (e.g., hash map for counts, set for voters) and define a simple interface (e.g., castVote, getResults). Consider extensibility for future features like weighted votes or multiple elections.

3. Leverage AI to Implement Quickly

Use AI to generate the initial code based on your design. Prompt with specific requirements and constraints, then review and refine the output to ensure it meets your standards.

4. Explain and Validate the Solution

Walk through the code, highlighting key decisions and trade-offs (e.g., time/space complexity, concurrency). Discuss how you would test edge cases and ensure correctness.

5. Extend and Iterate on Follow-ups

Be ready to adapt the solution for follow-up questions: e.g., adding real-time updates, scaling to millions of votes, handling fraud, or supporting multiple elections. Discuss architectural changes needed.

Key Points to Mention

Data structures: hash map for O(1) vote counting, set for preventing duplicate votes.
Concurrency and consistency: how to handle simultaneous votes, use of locks or atomic operations, and eventual consistency in distributed systems.
Scalability: sharding by candidate or region, using a message queue for vote ingestion, and caching for read-heavy workloads.
Product decisions: tie-breaking rules, vote weighting, audit trails, and user feedback (e.g., confirmation of vote).
Testing and monitoring: unit tests for edge cases, load testing, and metrics for vote counts and system health.
AI tooling usage: how to prompt effectively, review generated code, and integrate AI into the development workflow.

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