Apple·Machine Learning Engineer·Onsite - System Design / Architecture
Apr 2026
Apple ML Engineer system design round, one big open-ended question about building a full sentiment and topic extraction pipeline from scratch. They clearly wanted you to drive the conversation and surface trade-offs yourself rather than wait to be prompted, which I was not fully prepared for.
- Design an end-to-end ML system to extract sentiment and topics from a continuous stream of user feedback text, such as product reviews and support tickets, for a consumer product company.
- How would you handle topics that weren't in your original taxonomy, ones that start appearing in user feedback after your model is already deployed?
- Walk through the trade-offs between running inference as a nightly batch job versus a near-real-time pipeline. How do you decide which one to build?
- What labeling strategy would you use for training, given that you have a large unlabeled corpus and limited annotation budget?
- When would you choose a classical model like logistic regression on TF-IDF over a fine-tuned transformer, and when would you reach for a zero-shot LLM instead?
- How would you evaluate the system offline and then monitor it once it's live in production?
“This was the whole interview basically.”