← Anthropic Interview Insights

Anthropic·Software Engineer·Online Assessment (OA)·Intermediate

IntermediateRejected
May 2026

Summary

Took the Anthropic Fellows online assessment and scored zero. The algorithm itself wasn't hard, but the starter code and README were a mess of contradictions, and I spent most of my time just trying to figure out what format they actually wanted back.

Questions Asked (1)

Q1

Implement an algorithm using the provided class structure and return output in the format specified by the README.

Algorithms & Data StructuresAdaptability & Ambiguity
Author's notes

The algorithm itself was fine, nothing that would trip up anyone with real coding experience.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

First, clarify the problem requirements and constraints by asking targeted questions, then outline your algorithm and data structure choices before coding. Implement the solution using the provided class structure, ensuring the output format matches the README specification, and test with edge cases.

Pro tip: Demonstrate adaptability by acknowledging ambiguities and proposing a reasonable interpretation, then iterating if needed. Show maturity by discussing trade-offs and asking for feedback on your approach before diving into code.

1. Clarify Requirements

Ask questions to understand the problem scope, input/output formats, constraints, and edge cases. Confirm the expected behavior of the provided class structure.

2. Plan the Algorithm

Outline a step-by-step approach, choose appropriate data structures, and analyze time and space complexity. Consider alternative solutions and justify your choice.

3. Implement the Solution

Write clean, modular code within the provided class structure. Ensure the output format matches the README specification exactly.

4. Test and Validate

Walk through test cases, including edge cases, and verify correctness. If time permits, discuss potential optimizations or improvements.

Key Points to Mention

  • Clarifying questions to resolve ambiguity and align with expectations
  • Choice of data structures and algorithms with complexity analysis
  • Adherence to the provided class structure and output format
  • Handling edge cases and testing strategy
  • Trade-offs between different approaches and adaptability to feedback
  • Clean code practices and readability

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