Went through the Adobe machine learning engineer interview recently for a Sensei AI team role. "Sensei" is their ML platform brand. Sharing because MLE interview coverage for Adobe is sparse online.
The loop was six rounds total spread across two days for the virtual onsite.
Coding (2 rounds): Standard ML-adjacent coding. One round was essentially a data structures problem (nothing ML-specific, just algorithms), and one round asked me to implement a simple gradient descent loop in Python from scratch. No libraries, just numpy. They watched how I thought about numerical stability and step size.
ML fundamentals (1 round): Deep on theory. Bias-variance tradeoff, regularization, how you'd choose between L1 and L2, explain attention mechanisms at a conceptual level, when would you use a transformer vs. something simpler. They also asked about model calibration, which not everyone preps. Know Platt scaling at least in concept.
ML system design (1 round): Design a content recommendation system for Adobe Stock. Starting from raw event data through feature engineering, model selection, serving, and A/B testing. I spent a lot of time on the feature engineering and cold start problem because that's genuinely hard for a stock image use case. The interviewer seemed to like that I didn't jump straight to "just train a two-tower model" without justifying it.
Past project deep-dive (1 round): Very similar to the DE loop above: pick a project, they go three layers deep. What broke, what metric you picked and why, what you'd do differently.
Behavioral (1 round): Standard STAR. Adobe values cross-functional collaboration heavily in their behavioral questions. I got: tell me about working with PMs on an ML project where they wanted something you didn't think was technically feasible.
Model calibration and the cold start problem were the two things I'm glad I'd prepped. The gradient descent from scratch round would have gone badly if I hadn't done it at least once before walking in.