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Anthropic·Software Engineer·Take-home Assignment·Senior

Senior
Jun 2026

Summary

Got a coding and design exercise for a Software Engineer role at Anthropic, done in Python on Google Colab. Pretty broad scope, covering everything from requirements clarification to testing to reproducibility. More of a take-home style prompt than a typical leetcode grind.

Questions Asked (1)

Q1

You're given an open-ended coding and design exercise in Python to complete in Google Colab. Walk through your full approach: clarifying requirements, picking data structures, structuring the code into modules or notebooks, handling I/O and config, logging, error handling, writing and running unit tests, analyzing complexity, managing dependencies, and making everything reproducible and documented for reviewers.

System DesignTechnical Trade-offsAdaptability & Ambiguity
Author's notes

This is basically asking you to describe your entire engineering process in one shot, which sounds manageable until you realize how much ground it covers.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem's scope and constraints with the interviewer, then outline a modular design that separates concerns (e.g., core logic, I/O, config, tests). Emphasize reproducibility, documentation, and trade-offs, and walk through your implementation plan step by step, showing how you'd validate with tests and analyze complexity.

Pro tip: Treat the exercise as a mini production project: set up a clear project structure, use type hints and docstrings, and include a README with setup and run instructions. This demonstrates that you write code for others to read, run, and maintain—exactly what Anthropic values.

1. Clarify Requirements and Constraints

Ask questions to understand the problem's scope, expected inputs/outputs, performance needs, and any constraints (e.g., time, dependencies). Confirm assumptions and edge cases before designing.

2. Design Modular Architecture

Sketch a high-level design: separate core logic, I/O, configuration, and utilities into modules or notebook sections. Choose data structures based on access patterns and complexity, and justify trade-offs.

3. Implement with Robustness in Mind

Write clean, documented code with error handling, logging, and configurable parameters. Use type hints and docstrings, and handle edge cases gracefully.

4. Test and Validate

Write unit tests for core functions, covering normal and edge cases. Run tests in Colab, and use assertions or a testing framework. Analyze time/space complexity and note potential optimizations.

5. Ensure Reproducibility and Documentation

Pin dependencies, provide a requirements.txt or environment.yml, and include a README with setup and usage instructions. Structure the notebook with clear sections and markdown explanations for reviewers.

Key Points to Mention

  • Clarifying questions to resolve ambiguity and align on expectations
  • Choosing appropriate data structures and justifying trade-offs (e.g., time vs. space)
  • Modular code organization with separation of concerns (core logic, I/O, config)
  • Error handling, logging, and input validation for robustness
  • Unit testing with edge cases and complexity analysis
  • Reproducibility via dependency pinning, documentation, and clear notebook structure

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