← Back to Directory

Apple interview notes, page 13 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·Frontend Engineer·Technical Phone Screen

May 2026
Apple frontend round that went deep on React 18 internals. Not a vibe-check at all, they clearly wanted someone who had actually used these APIs under pressure, not just read the docs.
  • How does React 18's concurrent rendering work, and what problems is it actually solving?
  • Walk me through useTransition. When would you use it and what are the tradeoffs?
  • What's the difference between useTransition and useDeferredValue, and when would you reach for one over the other?
  • What is useId for and why does it matter for server-side rendering?
  • Explain useSyncExternalStore and why it exists specifically for concurrent mode.

“I started with the scheduler and interruptible rendering model, which felt right, but I fumbled explaining why it matters in practice.”

View Post

Apple·Machine Learning Engineer·Technical Phone Screen

May 2026
Apple ML engineer interview with a coding problem around building a basic regex parser. Not the most grueling round but it required more thought than I expected going in.
  • Implement a basic regex parser that supports a defined subset of regex operations.

“I went straight to recursion and kind of fumbled the base cases for a while.”

View Post

Apple·Product Manager·Onsite - Product Sense / Strategy

May 2026
Apple PM interview, one question about measuring FaceTime success. Short but the kind of question that sounds easy and then you realize you've been staring at a blank mental whiteboard for 30 seconds.
  • How would you measure the success of FaceTime?

“My first instinct was to go straight to call volume and retention, which in hindsight was pretty surface-level.”

View Post

Apple·Software Engineer·Hiring Manager Screen

May 2026
Interviewed for a BA role at Apple and got asked what I'd want to know before diving into any analysis. Short question, more to unpack than I expected.
  • What clarifying questions would you ask before starting an analysis?

“I rattled off a few things like 'what's the goal' and 'who's the audience' and felt pretty good about it, but in hindsight I was just listing obvious stuff.”

View Post

Apple·Software Engineer·Technical Phone Screen

May 2026
Apple SWE interview, got asked a pretty standard question about the map function. Not much else to report.
  • Can you explain how the map function works?

“Pretty basic one.”

View Post

Apple·Software Engineer·Technical Phone Screen

May 2026
Apple SWE interview with two parts back to back: a project deep dive and a coding problem. The coding portion had a twist where you actually had to write and run your own test cases, not just solve it.
  • Walk us through a past project you worked on, covering the problem, your role, the key technical decisions you made, and how it turned out.
  • Given two sorted integer arrays and a number k, return the k pairs (one element from each array) with the smallest sums. You also need to write your own test cases and run the code to verify it works.

“The follow-up questions are where this gets uncomfortable.”

View Post

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

May 2026
System design round at Apple for an ML Engineer role, basically a full hour of designing a CI/CD platform that had to handle everything from regular backend services to GPU model deployments. The scope was massive and I kept second-guessing how deep to go on each piece.
  • Design a CI/CD platform for a large AI product organization that supports backend services, model-serving APIs, data pipelines, and Kubernetes-based infrastructure across hundreds of services and multiple environments.
  • How would you handle model artifact versioning and ensure reproducible deployments for ML models in this platform?
  • Walk through your approach to secrets management, RBAC, and policy enforcement across multiple Kubernetes clusters in this platform.
  • How would you design the observability layer to cover build health, deployment status, service health, and model-specific health metrics?
  • How would you handle operational failure scenarios like flaky tests, failed deployments, queue backlogs, and cluster capacity limits at scale?

“I spent way too long on the pipeline triggering and build stages and then had to rush through the deployment strategies at the end.”

View Post

Apple·Data Scientist·Technical Phone Screen

May 2026
Interviewed at Apple for a data science or analytics role, got hit with an experimentation question that felt deceptively simple but had some real depth to it.
  • How would you verify that assignment to A/B testing groups was actually random?

“I started talking about running a chi-square test on the group sizes and checking for balance across key user attributes, which felt right.”

View Post

Apple·Software Engineer·Onsite - System Design / Architecture

May 2026
Apple system design round, one question about designing something along the lines of a CI/CD pipeline. Pretty standard format, nothing unusual about how it was run.
  • Design a CI/CD pipeline system similar to what a large AI platform company might use internally.

“Knew the basic components but kept second-guessing how deep to go on the build artifact storage vs the orchestration layer.”

View Post

Apple·Software Engineer·Technical Phone Screen

May 2026
Apple SWE interview that centered almost entirely on one meaty algorithm problem. The question had a lot of layers and the follow-ups kept coming, which I wasn't fully prepared for.
  • Given an array or stream of integers and a value k, return any valid ordering of the k most frequently occurring integers. Implement a heap-based solution, then walk through how you'd handle ties, negative numbers, very large inputs, and streaming data under memory constraints. Also analyze time and space complexity and describe a bucket-based alternative.

“This felt like one question that kept growing arms.”

View Post