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Apple interview notes, page 27 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 hardware engineer interview, open-ended EE technical round covering analog and digital fundamentals. Nothing behavioral, just straight engineering questions back to back. Felt more like a conversation than an interrogation, which I didn't expect.
  • How do you choose a pull-up resistor value, and what are the trade-offs involved?
  • What are the standard I2C clock frequencies, and what factors limit how fast you can actually push the bus?
  • How would you go about filtering noise from an analog signal using passive or active circuitry?

“This sounds like a textbook question until you're actually talking through it and realize you're juggling four things at once: rise time, power draw, noise immunity, and bus capacitance.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple coding interview with a classic array sorting problem. Pretty straightforward session, nothing too wild, but the constraint of doing it in-place with limited extra space is where it gets interesting.
  • Given an array of objects colored red, white, or blue (represented as 0, 1, and 2), sort the array in-place so all same-colored elements are grouped together.

“Classic Dutch national flag problem.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple SWE interview with a game logic question that sounds easy until you actually have to be thorough about it.
  • Walk through all the possible end game states in tic tac toe.

“I jumped straight to 'X wins, O wins, draw' and thought I was done.”

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

Jun 2026
System design round at Apple for an ML Engineer role, focused entirely on building a search service for an app store. Pretty deep question that touched everything from indexing pipelines to ranking to how you'd measure search quality after launch.
  • Design a search service for an app store where users type a query and get back a ranked list of apps. Walk through the full system: indexing pipeline, query path, storage, and how you'd handle scale.
  • How would you handle ranking in this search system, combining text relevance with signals like download counts, ratings, and recency?
  • How would you evaluate search quality for this system, both before and after shipping changes?
  • How would you design the storage and sharding strategy for a catalog of millions of apps that updates frequently?

“This question is deceptively wide.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple SWE interview with a classic algorithms problem. Not much to report beyond the question itself, but it's the kind of thing that separates people who've drilled it from people who haven't.
  • Given two sorted arrays, find their median.

“The naive merge-and-find approach works but they're clearly waiting for the binary search solution.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple SWE interview with a geometry/math coding question. Pretty short session, nothing too wild, but the 3D angle tripped me up more than it should have.
  • Given two points in 3D space, compute the distance between them.

“Blanked for a second on whether to use the standard Euclidean formula extended to three dimensions or something else.”

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

Jun 2026
Apple ML engineer system design round, one big question about building a Siri-style voice assistant end to end. Dense question that touched basically every layer of the stack, from ASR latency budgets to on-device privacy to hallucination detection. Left the room not totally sure how deep they wanted me to go on each piece.
  • Design a voice assistant response-generation system similar to Siri, covering the full pipeline from speech input to spoken output.
  • How would you ensure factual accuracy in the assistant's responses, and what do you do when the retrieval step returns nothing?
  • How would you handle personalization features like calendar and location access while keeping user data private?
  • What metrics and evaluation pipelines would you set up to measure whether this system is working well in production?

“This question is basically five questions stapled together.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple SWE interview that went deep into transformer internals. One question, but it had a lot of layers to it and I definitely underestimated how much they'd want on the complexity and numerical stability side.
  • Implement multi-head self-attention from scratch in PyTorch or NumPy. Given an input tensor of shape (batch_size, seq_len, d_model), write the full forward pass including linear projections for Q, K, V; splitting into h heads; scaled dot-product attention with optional padding and causal masks; head concatenation; and the output projection. Walk through the shape of every intermediate tensor, analyze time and memory complexity, and explain numerical stability concerns like softmax scaling and mixed precision.

“I got through the core implementation fine but stumbled when they pushed on shapes during the head-split step.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple data analyst interview with a product analytics case about a struggling sign-up flow. Pretty standard case format but the depth they expected was more than I anticipated.
  • A newly launched mobile app is seeing a 25% drop-off during the sign-up flow. How would you go about investigating this?

“I started with funnel breakdown by step which felt right, but I spent too long on the data side and didn't get to hypotheses fast enough.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple SWE interview with a classic stock problem. Pretty standard coding round, nothing that surprised me.
  • Given an array of daily stock prices, find the maximum profit possible from a single buy and sell transaction.

“Classic sliding window / greedy problem.”

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