Anthropic·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePass

Apr 2026Remote

Summary

Phone screen for a software engineer role at Anthropic. One coding question involving image processing, felt a bit rushed but got through it and passed.

Questions Asked (1)

Given a set of images, write a solution to process them (grayscale conversion, scaling, resizing) first for small images, then extend it to handle large images efficiently.

Technical Trade-offsSystem DesignAlgorithms & Data Structures

Author's notes

Time was tight and I wasn't moving fast enough, the interviewer had to nudge me along a couple times which was a little embarrassing.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a simple in-memory solution for small images. For large images, propose a chunked or streaming approach that processes images in tiles or batches to avoid memory issues, and discuss trade-offs between memory, speed, and complexity.

Pro tip: Emphasize that the core operations (grayscale, scaling, resizing) are the same; the challenge is adapting the I/O and memory strategy. Mention using libraries like OpenCV or PIL for correctness, but focus on the architectural shift from loading entire images to processing them in chunks.

1. Clarify Requirements and Constraints

Ask about image formats, expected sizes, memory limits, latency requirements, and whether the solution should be a library, service, or script. This ensures you design the right solution.

2. Design for Small Images

Propose a straightforward approach: load each image into memory, apply grayscale conversion, then scale/resize using standard libraries. Discuss time and space complexity.

3. Extend to Large Images

Introduce chunking or streaming: process images in tiles or strips, or use memory-mapped files. Explain how to handle operations like resizing that may require global context (e.g., using two-pass algorithms).

4. Address Trade-offs and Optimizations

Compare approaches: in-memory vs. chunked vs. distributed. Discuss trade-offs in memory usage, speed, code complexity, and potential use of GPU or parallel processing.

5. Summarize and Suggest Next Steps

Recap the solution, highlight key decisions, and mention testing strategies (e.g., unit tests for small images, stress tests for large). Suggest profiling to identify bottlenecks.

Key Points to Mention

Memory management: avoid loading entire large images into memory; use streaming or tiling.
Algorithm choice: grayscale conversion is per-pixel; resizing may require interpolation and can be done in chunks with overlap.
Library usage: leverage existing libraries (OpenCV, PIL, ImageMagick) for correctness, but understand their memory behavior.
Parallelism: process multiple images or chunks concurrently using threads, processes, or async I/O.
Trade-offs: balance between simplicity, performance, and resource usage; consider edge cases like very large images or limited memory.
Testing: validate with small images first, then scale up; use profiling to ensure efficiency.

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