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Apple interview notes, page 21 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

Jun 2026
Apple SWE interview with a graph/routing problem that looks deceptively like a simple BFS until you actually try to implement it. The complexity discussion at the end was where things got interesting.
  • You're given a set of bus route timetables with travel times between stops and wait times at each stop. Given a starting location, destination, and departure time, find the earliest possible arrival time at the destination. Transfers between routes are allowed. Also discuss time complexity and edge cases like no available path, multiple routes with equal travel time, and departure times that may or may not align with scheduled buses.

“This one took me a minute to even model correctly.”

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Apple·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Apple MLE interview that was basically one meaty system design problem about building a TTL cache from scratch. The question sounds simple but they really dig into the edge cases and follow-ups.
  • Design and implement an in-memory key-value cache where each entry has its own time-to-live (TTL). Include put, get, and optionally delete operations.

“I jumped straight to the implementation and almost skipped the clock source discussion entirely.”

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Apple·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Technical phone screen for an ML Engineer role at Apple. One question, deep dive into UNet architecture. The interviewer clearly wanted more than a surface-level answer.
  • What happens to UNet's performance and output quality if you remove the skip connections between the encoder and decoder?

“Started with the obvious answer about losing spatial detail and blurry boundaries, which felt right.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple technical screen for a software engineer role. One deep hardware-adjacent debugging scenario that took me a while to fully unpack. Not what I expected from a software interview.
  • A Wi-Fi chip under automated test works fine initially but stops transmitting after roughly 30 minutes. How do you systematically investigate the root cause?

“I went straight to firmware and host-driver state, which felt reasonable but I was ignoring the obvious.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple SWE interview, just one question about MVC. Pretty barebones experience to reflect on.
  • Can you explain the MVC architecture pattern?

“Knew what MVC was but fumbled a bit explaining how the model and controller actually interact in practice.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple EM interview with a low-key but tricky systems question about hashmap latency. Not a lot of context to go on, but the technical depth required was real.
  • Given specific parameters about a hashmap, calculate its expected read/write latency.

“This one tripped me up more than I expected.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple data engineer interview with a matrix manipulation problem. Pretty straightforward algorithmic round, nothing too wild, but the space complexity constraint is where things get interesting.
  • Given an M x N matrix, if any element is zero, set its entire row and column to zero. Do this in constant space.

“The naive approach is obvious and I went there first, which was probably a mistake to say out loud.”

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Apple·AI Engineer·Technical Phone Screen

Jun 2026
One coding round for an AI Engineer role at Apple. Pretty light on structure, just a single problem and you're mostly on your own to figure out what's being asked and test it yourself.
  • Given a collection of logs, find and return the top N logs.

“Easier than I expected for Apple.”

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Apple·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Deep technical interview at Apple for an ML Engineer role, focused almost entirely on annotation quality and LLM evaluation. One long question that kept branching into follow-ups. Felt like a research discussion more than a standard interview loop.
  • What is annotation agreement rate, and how do you measure it? Walk through the common metrics used for two raters, multiple raters, and varied data types, and explain what each one is actually correcting for.
  • What are the main limitations of inter-annotator agreement metrics? Think about class imbalance, skewed label distributions, ordinal versus nominal scales, and multi-label scenarios.
  • Compare using human annotators versus LLMs as judges. Cover cost, speed, consistency, bias, domain expertise, privacy, and transparency tradeoffs.
  • How would you concretely improve the reliability of an LLM-as-judge setup? What techniques would you actually implement?

“I knew Cohen's kappa and Fleiss' kappa reasonably well but fumbled a bit explaining what 'correcting for chance' actually means in practice.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple coding round for a software engineer role, structured around a kubernetes services dataset with three escalating parts. The first two were implementation-heavy, the third opened up into a design/agent discussion which I did not expect at all.
  • Given a collection of kubernetes services with various attributes and statuses, implement a set of filters based on specified requirements.
  • Using the same kubernetes services dataset, build a dependency chain between services and implement a way to query the status of a service by traversing that chain.
  • If this service filtering and dependency code were exposed as an MCP server, how would an agent integrate with it, and what strategies would you use to reduce noise in the agent's responses?

“Straightforward enough once you treat each filter as a predicate function.”

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