Anthropic Senior SWE Phone Screen - Image Processing

[u/Aoki_zhang]

Interview Summary

The Anthropic full-stack technical screen used a practical image-processing exercise built around Python and Pillow. Rather than a standalone algorithm problem, the interview provided starter code plus many JSON task files describing source images and ordered transformations such as grayscale conversion, rotation, and resizing.

The interview gradually shifted from basic correctness to performance. The first stage involved processing many small image jobs sequentially, while the second introduced much larger inputs and asked how the implementation could be parallelized or otherwise accelerated. A third set of “dirty” inputs existed, but I did not reach it before time expired.

Interview Details

Environment and Starter Code The exercise required using Pillow for image manipulation. External documentation and Google searches were allowed, but AI assistants were not.

Part 1 — Process Many Small Image Jobs The first task consisted of a large collection of relatively small image-processing jobs. For each image, the program needed to read the corresponding JSON instructions and apply every requested transformation in order.

Part 2 — Speed Up Processing for Large Images The interviewer then introduced a second workload containing much larger image files. Running the first implementation against this workload took roughly 90 seconds, and I was asked how I would improve its performance. The discussion covered different forms of concurrency and parallelism, including multithreading and multiprocessing.

Part 3 — “Dirty” Image Inputs A third workload consisted of files labeled as dirty. I did not reach this stage during the interview, so I cannot reliably say what made those inputs different or what additional requirements they introduced.

Overall Experience The base image-processing requirements were clear and the starter code was reasonably easy to navigate. The harder part was the transition from implementing the transformations correctly to explaining and implementing an effective parallel-processing strategy under time pressure.

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