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Apple interview notes, page 26 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 that went deep on hashmaps. Started with the basics but quickly turned into a systems-level conversation about resizing behavior and latency tradeoffs.
  • Explain how a hashmap works internally, including how bucket resizing is handled and how you would limit the latency spike caused by copying all entries into a new bucket during a resize.

“The first part was fine, walked through hashing, collision handling, load factor.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple SWE interview with a matrix rotation problem. A twist on the classic LC 48 but counter-clockwise, which tripped me up more than I expected for something that feels like it should be routine.
  • Given an n x n 2D matrix, rotate it 90 degrees counter-clockwise in place.

“I knew the clockwise version cold so I figured this was easy.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple SWE interview, one question about a project you built. Pretty light on detail but the kind of thing that can go sideways fast if you pick the wrong example.
  • Walk me through a piece of software you built.

“Picked a project I thought was impressive but ended up spending too long on the backstory and not enough on the actual technical decisions.”

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Apple·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Apple PM interview, just the one question about improving Apple Music. Short session, product sense focus, not much else to report.
  • How would you improve Apple Music?

“Classic product improvement question but Apple asking it about their own product adds a weird pressure.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple QA interview, one question that sounds deceptively simple until you actually sit down and try to structure it. The kind of open-ended product quality question where the scope is entirely on you to define.
  • You're handed a pair of AirPods for the first time with no prior knowledge of the product. Walk through how you'd build a test plan for them.

“My first instinct was to go straight into functional tests, pairing, audio quality, that kind of thing.”

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Apple·Product Manager·Onsite - Behavioral / Leadership

Jun 2026
Went through a PM interview at Apple and got hit with a behavioral question about self-teaching. Pretty standard stuff but it made me realize I hadn't prepped a crisp story for it.
  • Tell me about a time you taught yourself a new skill.

“I had a story ready but it came out kind of rambling.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple SWE interview with a scheduling problem that looks straightforward until you realize they want two answers out of one solution. Classic Apple move where the question has layers you don't see coming.
  • Given a list of meeting room booking intervals [start, end), first determine if all meetings can fit in a single room, then compute the minimum number of rooms needed if not.

“I jumped straight to the min-rooms part and almost forgot they also wanted the single-room boolean as a separate output.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple interview for a role involving conversion rate analysis. Not much to go on from what I remember but it was product analytics focused.
  • How would you find or calculate conversion rates for a product or funnel?

“Pretty open-ended and I wasn't sure how narrow to go.”

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Apple·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Apple ML engineer screen, pretty deep technically. The whole session was basically one long attention mechanism question that kept branching into follow-ups. Not a bad experience but you better know your transformer math cold.
  • Implement scaled dot-product self-attention from scratch. Given input X of shape (batch, seq_len, d_model) and projection matrices W_Q, W_K, W_V, compute the full attention output using softmax(Q K^T / sqrt(d_k)) V.
  • Extend your attention implementation to multi-head attention. How do you split and recombine the heads?
  • How would you implement a causal attention mask, and why is it needed?
  • What is the time and space complexity of self-attention, and where does it become a bottleneck?
  • What numerical issues come up with softmax in attention, and how do you handle them?

“I started okay, wrote out the projections and the matmul, but fumbled a bit explaining why we divide by sqrt(d_k).”

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Apple·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Got a pricing case question for what seemed like a product management role at Apple. Pretty short interview from what I can tell, just the one question but it had some real depth to it.
  • How would you set the price for a mid-range phone that has premium camera capabilities but costs more to manufacture?

“The cost angle is a bit of a red herring and I almost fell into it.”

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