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Apple interview notes, page 24 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·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Second round at Apple for an ML Engineer role, and they went deep on video compression algorithms. Pretty clearly tied to what I'd listed on my resume, so it wasn't totally out of nowhere, but the depth they expected was another story.
  • Walk me through how video compression algorithms work, and how you've applied them in your projects.

“They tied this directly to my project background so I knew it was coming in some form.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple SWE coding round, two problems back to back. Both were algorithmic, one pretty standard and one with a follow-up that added real complexity. Felt okay leaving but not great.
  • Given a positive integer, repeatedly replace it with the sum of the squares of its digits. Does the sequence eventually reach 1, or does it loop forever without hitting 1?
  • Find the shortest path between two cells in a binary grid, where 0 is open and 1 is blocked. Then, extend your solution to allow converting up to k blocked cells into open cells along the path.

“I'd seen this before so the cycle detection angle clicked fast.”

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Apple·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Apple SWE interview with three algorithmic problems back to back. The questions ranged from coordinate compression to grid connectivity to data structure design, so it was a pretty broad spread for a single session.
  • Given n inclusive time intervals representing users being online, return a list of disjoint time segments with the count of concurrent users in each segment. Timestamps can be very large, so a naive array approach won't work.
  • Fill an R x C grid with given counts of vegetable types such that every cell is filled and all cells of each type form a single 4-directionally connected region. Return any valid grid or indicate it's impossible.
  • Design a data structure supporting insert, getMin, getMax, popMin, and popMax on a multiset of integers. Discuss time complexity and when a bucket/counting approach is better or worse.

“The large timestamp constraint is the whole point of the problem.”

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Apple·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Apple system design round for a software engineer role. The whole session was basically one deep problem about constrained hardware, and they really wanted you to think through every layer of it rather than just sketch a high-level architecture.
  • Design an inventory management system that runs on a machine with no SSD and very limited RAM, relying entirely on spinning disk storage. The system needs to support reads, updates, and conditional reservations (e.g. reserve a unit only if available quantity is greater than zero). Walk through data layout, indexing, caching, read path, write path, and latency considerations.

“This one took me a minute to even process.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple SWE interview with a geometry-heavy coding problem that felt more like a math puzzle than anything I'd practiced. The question was deceptively clean on the surface but had a lot of edge cases hiding underneath.
  • You're standing at a fixed point on a hill surrounded by trees at various (x, y) coordinates. Given a viewing angle, find the maximum number of trees you can see in a single frame. Trees in the exact same direction only count once since closer ones block the rest.

“The blocking condition is what tripped me up first.”

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Apple·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jun 2026
One round at Apple for an ML Engineer role, just a single behavioral question about conflict resolution. Pretty short interaction, nothing technical.
  • Can you walk me through a conflict you've faced and how you handled it?

“I had a story ready but it felt a bit thin in the moment.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple SWE interview, got hit with the classic invert a binary tree problem. Pretty standard stuff but still a little nerve-wracking when it's Apple.
  • Invert a binary tree.

“Knew this one cold from practice but still fumbled the explanation a bit.”

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Apple·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Apple hardware engineer interview with a product-design exercise focused on thermal modeling. One meaty technical question that took up most of the session, very applied, less theory and more 'show me you can build something real'.
  • Design a module that models a thermal conduction system using an electrical resistance analogy. Walk through how you'd define the thermal resistances (conductive, convective, radiative), set boundary conditions, handle heat sources, and describe how the module's interface and parameters would plug into a larger composed thermal system.

“This took me a second to orient to because I kept wanting to jump straight into equations.”

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Apple·Frontend Engineer·Technical Phone Screen

Jun 2026
Apple frontend interview that was basically one meaty coding question about debounce, but they pushed hard on the variants and edge cases so it ended up taking most of the session.
  • Implement a debounce function in JavaScript or TypeScript that delays invoking a callback until a specified number of milliseconds have elapsed since its last call.

“Started fine, got the basic setTimeout and clearTimeout skeleton down pretty quick.”

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Apple·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Apple ML engineer interview with a hands-on coding question around image similarity search. Pretty focused on systems thinking as much as the code itself, which I wasn't fully expecting.
  • You're given a feature extractor, a similarity function, and a corpus of images. Implement a system that takes a query image and returns the most similar image from the corpus.

“I jumped straight into the implementation and got the basic wiring done fine: embed the corpus, embed the query, run pairwise similarity, return the top match.”

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