Apple interview notes, page 22 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 low-level C coding problem centered on an airplane seat reservation system. Pretty involved for a single session, lots of edge cases to think through on the fly.

Implement a set of C functions for an airplane seat reservation system: create an airplane struct with configurable rows, seats per row, and two aisle positions; print its layout; free all allocated memory; and reserve a specific seat by name, with an optional fallback to a nearby seat if the requested one is taken.

“This was a lot to hold in your head at once.”

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Apple·Software Engineer·Hiring Manager Screen

Jun 2026

Apple SWE interview, one question that sounds deceptively simple but has a lot of moving parts once you actually try to answer it.

If two clients are requesting very different features for the same product, how do you decide which one to prioritize?

“I started talking about impact and user base size, which felt right, but then I realized I was just listing criteria without actually making a call.”

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Apple·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jun 2026

Interviewed for an ML Engineer role at Apple. One question tripped me up badly due to a misread, and I'm still a bit annoyed at myself about it.

How would you respond if someone told you that you lack business sense?

“I completely misread this.”

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Apple·Machine Learning Engineer·Hiring Manager Screen

Jun 2026

Apple MLE interview that felt more like a team-fit conversation than a technical grilling. A lot of the questions were about what I'd been building and how I thought about the work, which I wasn't fully expecting.

What does your team build, and who are the users or customers of that product?
What are the main use cases for the product your team works on?
How does your team monitor customer-facing KPIs? Which metrics do you track, how are they collected, and what triggers an alert or response?
What programming languages and tools do you use on a daily basis?
If you moved into a more production-focused or applied role, do you think you'd miss doing research?

“Pretty standard opener but I fumbled it slightly because I was trying to be too precise about the user segmentation.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026

Apple SWE interview with a grid-based island problem. Pretty standard algorithmic stuff but the follow-up caught me a bit flat-footed.

Given a binary matrix of 0s and 1s, implement a function sizeOfIsland(grid, r, c) that returns the number of cells in the island containing cell (r, c), or 0 if the cell is water or out of bounds. You can mutate the grid or use a separate visited structure. Walk through your algorithm, state your base cases, and analyze time and space complexity.
Adapt your solution to find the size of the largest island across the entire grid.

“I went with DFS and chose to mutate the grid by flipping visited 1s to 0s.”

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

Jun 2026

Apple ML Engineer interview focused almost entirely on one massive system design question about building a search keyword recommendation pipeline for an app marketplace. It was a deep, open-ended session and I left feeling like I'd only scratched the surface of what they were actually looking for.

Design an ML pipeline that generates search keyword recommendations for an app marketplace. Given a query like 'games,' how would you produce diverse, typed suggestions (e.g., puzzle, RPG, racing) with high relevance and coverage?
What data sources would you use to train and improve this recommendation system, and how would you collect labels or feedback signals?
How would you evaluate this system both offline and online, and what guardrails would you put around an A/B test for it?
After the system is live, how would you handle continual or online training? Walk through streaming feedback ingestion, feature freshness, and how you'd detect and respond to model drift.
How would you handle cold start for new apps or new queries, spam and abuse in suggestions, and fairness across app developers?

“This thing is enormous.”

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Apple·Product Designer·Onsite - Product Sense / Strategy

Jun 2026

Apple product design interview, one question about designing an automatic recycling bin. Pretty open-ended, which I was not fully prepared for.

Design an automatic recycling bin.

“I went straight into features without asking who this is for, which was a mistake I noticed mid-answer.”

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Apple·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Apple EM interview with a system design question about building a queuing system for a call center. Pretty focused, just the one problem but it had enough depth to keep you busy for a while.

Design a queuing system for a call center with N representatives, where incoming requests exceed N at any given time and each request takes roughly 2 to 4 seconds to process.

“Spent the first few minutes just thinking about the bounded processing time and whether that changed anything about the queue design.”

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Apple·Data Scientist·Onsite - Behavioral / Leadership

Jun 2026

Apple Data Scientist interview that went deep on conflict resolution and stakeholder pressure. The questions were less about modeling chops and more about how you hold your ground when everyone is pushing back. Felt more like a behavioral gauntlet than a technical screen.

Tell me about a project where you disagreed with your tech lead on which model to use, especially under a tight deadline. How did you frame the trade-offs, bring in data, and get the right people aligned?
What experiments or analyses did you run independently to validate your position in that disagreement?
Say an external client is pushing hard to ship right now and keeps brushing off your technical concerns. How do you set limits, communicate risk, and keep the relationship intact?
If that client escalates while your manager is out of reach, what do you actually do in the next 24 hours? And how do you document decisions so nobody can relitigate them later?

“This one took me a second to land on a good example.”

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Apple·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jun 2026

Apple ML Engineer interview, behavioral round focused on impact measurement. One meaty question about a real career win, but the follow-up on metrics and hindsight made it trickier than expected.

Tell me about a major achievement in your career. Walk through the problem, what you did, why it mattered, and how you actually measured the impact. Also be ready to explain how you defined success at the start and what you'd change looking back.

“I had a solid story ready but fumbled the metrics part.”

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