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Apple interview notes, page 11 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·Software Engineer·Technical Phone Screen

May 2026
Apple SWE interview with a library design question that looked deceptively simple on the surface. The card deck problem packed in a lot more than I expected once they started asking about bias guarantees and complexity.
  • Design a playing-card deck library with two methods: shuffle(), which returns a uniformly random permutation of a standard 52-card deck, and order(), which returns the cards sorted by rank then suit (using the ordering Clubs, Diamonds, Hearts, Spades within each rank, starting from 3 up through Ace). Define your data structures, explain how you guarantee an unbiased shuffle, and analyze time and space complexity.

“I started with the data structure and defined a Card as a pair of rank and suit enums, deck as a fixed array of 52.”

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Apple·Software Engineer·Technical Phone Screen

May 2026
Apple SWE interview with a tricky oracle-based median problem. No standard library calls, no direct access to the data, just a query interface and your brain. Made me rethink how much I rely on just sorting things.
  • You have N numbers you can't see directly. You can query any value x and get back how many of the N numbers are greater than x and how many are less than x. Find the median. Also discuss how query count scales, how to deal with duplicates, and what changes when N is even.

“This one took me a minute to even understand what they were asking.”

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Apple·Software Engineer·Technical Phone Screen

May 2026
Apple SWE interview with a low-level systems-flavored coding question about processing a string too large to fit in memory. Pretty niche problem and I wasn't expecting it.
  • You have a string that's too large to fit into memory on a single machine. How do you split it on spaces and count the number of spaces?

“My first instinct was to just load it and scan, which is obviously wrong given the constraint.”

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Apple·Product Manager·Onsite - Product Sense / Strategy

May 2026
Apple PM interview, one product design question about a pretty unexpected topic. Short session but it made me think harder than I expected.
  • How would you design a better dog collar?

“I blanked for a solid few seconds because I kept waiting for the real question.”

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Apple·Software Engineer·Technical Phone Screen

May 2026
Apple SWE interview that came down to a Sudoku Solver problem. Not much else to go on, but it's the kind of question that sounds manageable until you're actually in it.
  • Implement a Sudoku Solver.

“Backtracking is the obvious path here but getting the implementation clean under pressure is another thing entirely.”

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Apple·Machine Learning Engineer·Hiring Manager Screen

May 2026
Apple MLE interview, resume deep-dive round. One big open-ended question that sounds easy until you realize they want the full picture: problem, your actual contribution (not the team's), trade-offs, and impact. Felt like a lot to cover without rambling.
  • Walk me through your resume with a focus on your most relevant projects. For each one: what problem were you solving, what did you personally contribute, what trade-offs did you navigate, and what was the actual impact?

“This question sounds like a warm-up but it really isn't.”

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Apple·Software Engineer·Technical Phone Screen

May 2026
Phone screen for a software engineer role at Apple. Just one coding question, nothing else to report.
  • Given a 2D board of characters and a list of words, find all words that exist in the board. A word can be constructed from letters of sequentially adjacent cells (horizontally or vertically neighboring), and the same cell may not be used more than once per word.

“Classic trie plus DFS backtracking problem.”

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Apple·Machine Learning Engineer·Onsite - System Design / Architecture

May 2026
System design round at Apple for an MLE role, focused entirely on designing a news feed service at scale. The question covered a lot of ground and I wasn't fully prepared for how deep the ML side would go alongside the infra pieces.
  • Design a news feed service similar to Facebook, Twitter, or Instagram. Walk through functional requirements, scale considerations, storage, ranking, and how the architecture evolves from 1 million to 1 billion users.

“This one sprawled in every direction.”

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Apple·Machine Learning Engineer·Technical Phone Screen

May 2026
Apple MLE interview focused heavily on computer vision fundamentals, a Bayes theorem probability problem, and a production debugging scenario. Pretty rigorous across the board, more depth expected than I anticipated.
  • Walk through the core ML concepts that matter most in computer vision work: bias-variance tradeoff, overfitting, class imbalance, evaluation metrics, threshold selection, calibration, and data leakage.
  • 5% of images in a stream are actually blurry. A blur detector catches 90% of truly blurry images but also flags 10% of sharp ones. If an image gets flagged, what's the probability it's actually blurry?
  • A vision model that was working fine at launch has started degrading in production. How do you systematically diagnose what went wrong and figure out what to fix?

“This felt like a warm-up but it wasn't.”

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Apple·Software Engineer·Onsite - Coding / Algorithms

May 2026
Apple SWE coding round, three algorithm questions back to back with no actual coding required, just talk through your approach. Pretty classic stuff but the pacing was tight.
  • Given two sorted integer arrays, merge them into a single sorted array containing all elements from both.
  • Given an array of daily stock prices, find the maximum profit achievable from a single buy-sell transaction. Return 0 if no profit is possible.
  • Given a string of parentheses and brackets, determine whether the string is valid (every opener has a matching closer in the correct order).

“Two pointer approach, pretty textbook.”

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