Inside Anthropic's Interview Process: A Performance Take-Home Goes Open Source

Ever wondered what kind of problems top-tier AI companies like Anthropic use to evaluate engineering candidates? Wonder no more. They've just open-sourced one of their original performance-focused take-home assignments. It's a rare peek behind the curtain at the practical, systems-level thinking they value.

This isn't just a leaked question; it's the full, runnable project they used to assess candidates. For developers, it's a fantastic benchmark to test your own skills against, a learning tool for performance optimization, and a conversation starter about what "good code" really means in a high-stakes environment.

What It Does

The repository contains a complete, self-contained performance engineering problem. You're given a working Python script that processes data, but it's deliberately inefficient. The core task is to analyze, profile, and dramatically improve its performance—reducing both runtime and memory usage. It simulates a real-world scenario where you inherit legacy code that needs to be scaled effectively.

Why It's Cool

The cool factor here is multi-layered. First, it's authentically theirs. This isn't a sanitized example; it's the actual problem candidates received, giving you a direct taste of Anthropic's engineering culture. Second, the problem is beautifully crafted. It's complex enough to separate strong candidates but focused enough to be tackled in a few hours. It forces you to think about algorithmic complexity, data structures, Python's internals, and measurement.

Most importantly, it emphasizes empirical optimization. You can't just guess what's slow. You need to profile, form a hypothesis, improve, and measure again. This mirrors real performance work far more than abstract algorithm puzzles. Opening it up also invites the community to share and compare their optimization approaches, turning a private test into a public workshop.

How to Try It

Ready to take the challenge for yourself? Getting started is straightforward.

1. Clone the repo:

   git clone https://github.com/anthropics/original_performance_takehome.git
   cd original_performance_takehome
   

2. Set up a Python environment (3.8+ recommended) and install the single dependency:

   pip install -r requirements.txt
   

3. Run the original, slow version to see the baseline:

   python slow_code.py
   

4. Dive in. Read the README.md for the full problem statement and constraints. Then, start profiling. Use cProfile, timeit, or your favorite tool to find the bottlenecks. Write your improved version in a new file and test it against the provided validation logic.

There's no single "correct" answer, just a path toward a faster, more efficient solution.

Final Thoughts

This release is a gift for developers who enjoy the puzzle of optimization. Whether you use it as a personal challenge, a study group exercise, or just a fascinating codebase to read through, it provides concrete insight into what a leading AI company considers meaningful engineering work. It reminds us that performance isn't about being clever for cleverness's sake—it's about methodical analysis, informed trade-offs, and verifiable results. Give it a shot and see how your optimization skills stack up.

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