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Apple interview notes, page 18 of 38

FAANG / Big Tech

Most of these Apple write-ups come from Software Engineers (212), then Machine Learning Engineers (85) and Data Scientists (32). The most reported rounds are the technical phone screen (217), the system design round (48), the product sense round (29) and the behavioral / leadership round (22).

372 interview notes · updated Oct 2026

Apple·Product Manager·Onsite - Product Sense / Strategy

May 2026
Apple PM interview, got hit with a product metrics question about the iPad Pro. Pretty standard for Apple but the scope of the product made it trickier than I expected.
  • How would you measure the success of the iPad Pro?

“I started with revenue and units sold and immediately felt the interviewer cooling off.”

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Apple·Product Manager·Onsite - Product Sense / Strategy

May 2026
Apple Maps cost estimation question, pretty classic product sense interview but the scope caught me off guard more than I expected.
  • How would you estimate the cost of developing new features for Apple Maps?

“I started with engineering headcount and then kind of spiraled into infrastructure costs, licensing for map data, QA cycles.”

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Apple·Software Engineer·Technical Phone Screen

May 2026
Apple SWE interview with a classic data structures problem. Nothing too surprising but the O(1) constraint is where people usually trip up.
  • Design a data structure that supports insert, remove, and getRandom operations, all in O(1) average time.

“The getRandom part is what makes this tricky.”

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Apple·Data Scientist·Technical Phone Screen

May 2026
Apple DS interview that was basically a pandas workout disguised as a product analytics exercise. Four questions built on each other, starting simple and ending with a performance optimization discussion. Not a bad format actually, you can tell where your gaps are pretty fast.
  • You're given a TSV file where each line contains a user's page-visit history as timestamp-page pairs separated by tabs. Parse it and return the page with the highest total visit count.
  • For each visit in the dataset, compute how long a user spent on a page (the difference between the current timestamp and the next one). Which page has the greatest total residence time across all users?
  • Treat each user's ordered sequence of page visits as a complete path (like 'A→B→C'). Return the most common path across all users.
  • The residence-time calculation in question 2 can be slow with explicit loops. Rewrite it using vectorized operations and explain why it's faster.

“Pretty straightforward warm-up.”

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Apple·Machine Learning Engineer·Hiring Manager Screen

May 2026
Interviewed for an ML Engineer role at Apple. Only one question worth noting, and it was a soft one that I fumbled pretty badly.
  • Why do you want to work at Apple?

“Led with the brand appeal, which, looking back, was a mistake.”

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Apple·Data Scientist·Technical Phone Screen

May 2026
Apple DS interview that was basically one long case study on fraud detection modeling. Technically dense, covered everything from confusion matrix math to threshold derivation to online experiment design. Left feeling like I'd been put through a blender.
  • Given a fraud detection model with a 0.7% base rate and specific cost parameters (flagging costs $3, friction on legit order costs $1, missed fraud costs $120), compute precision, recall, F1, and expected cost per order for two models at threshold 0.5. Which model is better under these costs?
  • Derive the cost-optimal classification threshold as a general function of calibrated posterior probability and the cost matrix. Then apply it to this specific fraud scenario assuming perfect calibration.
  • Compare PR-AUC versus ROC-AUC for highly imbalanced classification problems. How do calibration metrics like Brier score and expected calibration error factor in, and what does decision curve analysis add?
  • Design an offline evaluation plan that stays robust when fraud prevalence shifts, and describe a safe online A/B test with appropriate guardrails like manual review SLAs, false accusation rate caps, and drift holdouts. How would you monitor the model post-launch for concept drift and fairness across user segments?

“The metrics part was fine, I can do confusion matrix math in my sleep.”

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Apple·Software Engineer·Technical Phone Screen

May 2026
Apple SWE interview with a graph problem that looks straightforward until you actually have to code it under pressure. One question, cycle detection, and the clock is ticking.
  • You're given an undirected graph that was originally a tree with n nodes, but one extra edge was added creating exactly one cycle. Given the edge list, find and return the edge that can be removed to restore it to a tree. If there are multiple valid answers, return the one that appears last in the input.

“Union-Find is the clean solution here and I knew that going in, but I second-guessed myself halfway through and started rambling about DFS instead.”

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Apple·Software Engineer·Technical Phone Screen

May 2026
Apple data engineer screen, one coding question, pretty standard stuff. The problem itself wasn't hard but I always second-guess my Roman numeral logic mid-interview.
  • Write a function to convert a Roman numeral string to an integer.

“I've done this problem before so I wasn't totally lost, but I fumbled explaining the subtractive case logic out loud.”

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Apple·Product Manager·Onsite - Product Sense / Strategy

May 2026
Apple PM interview, product fundamentals focus. Two questions back to back on MVP concepts, pretty standard stuff but the prioritization follow-up had more depth to it than I expected.
  • What is a minimum viable product?
  • How do you decide which features to include or cut when scoping an MVP?

“Straightforward definition question but I overcomplicated it.”

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Apple·Data Scientist·Technical Phone Screen

May 2026
Apple data scientist interview that was basically a single monster question disguised as a whole technical round. They handed me this massive random forest from scratch prompt and I spent the better part of an hour trying to keep all the moving parts in my head at once.
  • Implement a binary classification Random Forest from scratch for 200,000 samples and 100 mixed features under a 2 GB memory budget. Cover CART with Gini impurity, missing value handling, categorical splits without one-hot encoding, bootstrap and feature bagging with reproducible seeds, OOB evaluation including ROC-AUC and PR-AUC, a reliability diagram, Platt scaling vs isotonic regression calibration, severe class imbalance handling with a 10x FN/FP cost ratio, thread-safe parallelization with complexity analysis, and warm-start/streaming with concept drift detection via OOB metrics. Write pseudocode for train(), predict_proba(), and OOB evaluation and justify every design choice.

“I knew random forests reasonably well going in but this question is basically five separate hard questions stapled together.”

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