Finished a Disney ML engineer interview loop last month for a role on the personalization team, which sits under Disney+. Decided to write it up because the existing info online is mostly about SWE roles and this was pretty different.
Quick context: I'm 8 years in ML, mostly NLP and recsys. Was interviewing for a senior IC role.
The loop:
Phone screen with a hiring manager first, which is unusual. He asked me to walk through a recommender system I'd built end to end: feature engineering, model architecture choices, how I handled cold start, and how I measured success. This is basically the template for the whole loop, so get a solid story ready.
Technical rounds (3 total):
ML depth round: They gave me a scenario. Disney+ wants to improve content discovery for users who have different profiles across Disney, Hulu, and ESPN content. Walk me through how you'd design the recommendation model. It's an open-ended systems design question dressed up as ML. They wanted to see: how you define the problem, what signals you'd use (watch time, explicit ratings, thumbnail clicks), how you'd handle multi-domain content (sports vs kids vs blockbusters are really different), and how you'd evaluate offline vs online. I talked a lot about two-tower models and A/B testing frameworks.
Coding round: Not Leetcode. They had me write Python code to process and aggregate user engagement logs. Think pandas/numpy, not graph algorithms. Wrote a feature engineering pipeline from raw event data. Pretty practical.
Research/depth round: They asked me to present a recent paper or project I found interesting and explain how I'd apply it. I picked a retrieval-augmented reranking paper. This round felt more like a seminar than an interview.
Behavioral: One dedicated behavioral round, Disney-flavored. Biggest question: tell me about a time your model made a recommendation that felt technically correct but wrong for the user. They care about the human side of ML, not just the metrics.
Things that stood out: Disney takes cold start seriously because of the breadth of their content. They also care about latency (sub-100ms serving). And they're big on experimentation culture, so being able to talk about A/B test design, holdout sets, and statistical power matters.
Leveling-wise they offered senior MLE. Base was in the $180-200k range for LA, with RSUs that are... fine. Not Netflix ML money. Offer came about 6 weeks after I applied.