Apple interview notes, page 32 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

Apple MLE screen with a coding problem that looks deceptively simple until you realize they want you to throw out the DP solution and think linearly. Pretty focused session, just the one algorithmic problem plus a follow-up discussion.

Given two strings, determine whether they are exactly one edit distance apart, where an edit is an insertion, deletion, or substitution of a single character.

“My first instinct was full edit-distance DP and I started going down that road before they steered me away from it.”

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Apple·Software Engineer·Hiring Manager Screen

Jun 2026

Short Apple SWE screen, two questions and that was pretty much it. Nothing too wild but the project question took more thought than I expected.

Walk me through your career history.
Tell me about a project you're proud of.

“Pretty standard opener.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026

Apple SWE coding screen, one question the whole time. The problem looked straightforward but had enough constraints to trip you up if you weren't careful about complexity and the no-built-in-sort rule.

Given an unsorted integer array of roughly 75 elements that may contain duplicates, zero out all duplicate occurrences (keeping only the first) and then sort the result in non-decreasing order. You cannot use any built-in sort functions or library sorting implementations.

“The array size being ~75 is a hint they don't need an O(n log n) solution but they still want you thinking about it.”

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Apple·Software Engineer·Onsite - Coding / Algorithms

Jun 2026

Did an onsite at Apple for a software engineer role. Round 1 was a coding question, nothing too wild, but the details are pretty sparse on my end.

Solve a LeetCode-style coding problem presented during the first onsite round.

“Not much to say here.”

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Apple·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Apple MLE coding screen, one question, pretty standard sliding window type problem. Nothing crazy but I definitely overthought the approach at first.

Given an array of daily stock prices, find the maximum profit you can make by buying on one day and selling on a later day. Return 0 if no profit is possible.

“Started by thinking about brute force O(n^2) out loud which was a mistake, felt the energy shift a bit.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026

Apple SWE interview that went deep on load balancer design, specifically the IP-picking layer. Started straightforward and got progressively harder once weights and scale came into play.

Given a list of IP addresses, implement a function that returns a uniformly random IP from the list.
Each IP has an associated positive weight. Implement a function where the probability of returning a given IP is proportional to its weight.
How would you handle thread safety, dynamic updates to the IP list or weights, and sub-millisecond pick latency at high QPS?

“Warmup question, basically just pick a random index.”

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Apple·Technical Program Manager·Recruiter / HR Screen

Jun 2026

Interviewed for a TPM role at Apple, and the question was pretty much just the standard 'why do you want to work here' type thing. Not much to report.

Why do you want to work at this company?

“Pretty much a softball but I still fumbled it a bit.”

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

Jun 2026

Apple ML engineer interview, system design round focused entirely on multimodal embeddings. Dense topic, they went pretty deep on every layer of the stack from architecture choices down to serving.

Design a multimodal embedding model for our domain. Walk through the full system from problem framing to serving.
How would you structure the model architecture for handling multiple modalities, and what does the shared embedding space look like?
What loss functions would you use to train the model, and how do you handle hard negatives?
How would you approach training at scale, including batch composition and distributed training setup?
What offline and online metrics would you use to evaluate the embedding model?

“This was the whole interview, basically.”

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Apple·Machine Learning Engineer·Onsite - Multi Round

Jun 2026

Apple ML Engineer loop, and the whole thing basically came down to one big project deep-dive. They wanted real specifics, not just vibes about what you worked on.

Walk me through two recent projects you led or contributed to significantly. For each one, cover the problem, your role, the architecture or approach you chose, key tradeoffs, timeline, and measurable outcomes.
What was the hardest challenge you faced in one of those projects, and how did you resolve it?
Looking back, what would you do differently on one of those projects?
How did you collaborate with people outside your immediate team, and how did you maintain quality when timelines were tight?

“This is where I stumbled a bit.”

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Apple·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

Apple PM interview with one strategy question about the trade war and its impact on the business. Not a lot of context to go on but it's the kind of question that can spiral fast if you're not careful about scoping.

How does the US-China trade war impact Apple as a business?

“This one is deceptively broad.”

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