← Back to Directory

Apple interview notes, page 14 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

May 2026
Apple ML engineer interview covering vision encoder fundamentals and LLM optimization. Two distinct technical areas back to back, felt more like a depth check than a broad survey.
  • Walk me through the core concepts behind a vision encoder and how you would train one.
  • What are the main performance bottlenecks in large language models, and how do you approach memory and latency optimization?

“Talked through patch embeddings, attention over spatial tokens, pretraining objectives.”

View Post

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

May 2026
Apple MLE system design round focused entirely on building a search and content discovery system for a news app from scratch, no ML model in place yet. Pretty open-ended and covered a lot of ground, from ingestion pipelines all the way to how you'd eventually layer in ranking models.
  • Design a search and content discovery system for a news app like Apple News, starting with no trained ranking model. Users can search for articles, topics, and publishers. How do you make results relevant, fresh, safe, and somewhat personalized?
  • How would you handle article ingestion and extract useful metadata at scale?
  • Without a trained model, what signals would you use to rank search results?
  • How would you handle content freshness, and what tradeoffs does that create?
  • How would you approach safety filtering and editorial signals in the ranking pipeline?
  • What logging and evaluation strategy would you put in place from day one, and how would that set you up to eventually move to ML-based ranking?

“This was the whole interview, basically.”

View Post

Apple·Data Scientist·Technical Phone Screen

May 2026
Apple Data Scientist technical screen focused on deriving the logistic regression loss from scratch, including regularization. Pretty standard ML theory question but they wanted you to walk through every step out loud.
  • Write the logistic regression loss function for binary classification, including the per-example loss, the total loss over n examples, and optionally the L2-regularized objective.

“I knew this cold but still fumbled the notation a bit under pressure.”

View Post

Apple·Machine Learning Engineer·Technical Phone Screen

May 2026
Apple ML Engineer interview with a meaty coding question that was basically a mini search engine project. One question, lots of moving parts, and they expected you to talk through design decisions out loud the whole time.
  • Build a bag-of-words text similarity search engine from scratch. This includes tokenization with Unicode and optional stopword/stemming support, TF-IDF document vectors, cosine similarity scoring, and a top-k retrieval function. Discuss complexity and how you'd scale it.

“This is the kind of question where you think you know where it's going and then it just keeps going.”

View Post

Apple·Machine Learning Engineer·Onsite - Behavioral / Leadership

May 2026
Apple ML Engineer interview, behavioral round focused on how you manage competing priorities across multiple projects. One question but they really wanted you to go deep on it.
  • When you're running multiple projects at the same time, how do you decide what gets attention? Walk through a real example covering how you weighed urgency against impact, how you explained trade-offs to stakeholders, what you cut or pushed out, and how you kept tabs on everything in parallel.

“This question is deceptively hard to answer well.”

View Post

Apple·Software Engineer·Technical Phone Screen

May 2026
Apple SWE interview, got a classic matrix rotation problem. Pretty standard coding round, nothing too surprising.
  • Given an n x n 2D matrix representing an image, rotate it 90 degrees clockwise in-place.

“The in-place constraint is what makes this annoying.”

View Post

Apple·Machine Learning Engineer·Onsite - Behavioral / Leadership

May 2026
Apple ML Engineer interview with a behavioral question that had more teeth than I expected. The resource allocation angle made it feel more like a product or engineering lead question than a pure ML one.
  • Describe a situation where you needed to push for additional resources (headcount, budget, infrastructure, or timeline) to handle competing product demands. How did you build the case, who did you need to convince, what data or risk analysis did you bring, and how did it turn out?

“This one tripped me up a bit because I went straight into the story before thinking about what they actually wanted to hear.”

View Post

Apple·Software Engineer·Onsite - Multi Round

May 2026
Someone who cleared the Apple recruiter, HM, and technical coding screens is now prepping for the full virtual panel loop and asking the community for calibration help. The initial DSA screen involved implementing a HashMap and Queue from scratch, and they're trying to figure out whether the panel escalates in difficulty or stays in the same lane, especially given the platform/infrastructure slant of the role.
  • Implement a HashMap and a Queue from scratch.
  • For a platform or infrastructure-focused role, how do concurrency, API design, or real-world system constraints factor into the technical interviews compared to standard algorithm problems?
  • Do different interviewers in a panel loop deliberately cover distinct topic areas, or is there significant overlap across rounds?

“This was the technical screen question.”

View Post

Apple·Machine Learning Engineer·Technical Phone Screen

May 2026
Coding screen for an MLE role at Apple, focused on a classic string problem that turned out to have more depth than I expected. The interviewer pushed past the naive solution pretty quickly.
  • Implement a function to check whether a string is a palindrome, ignoring non-alphanumeric characters and treating uppercase and lowercase as equal.

“Started with the slice reversal approach because it's clean and readable.”

View Post

Apple·Machine Learning Engineer·Hiring Manager Screen

May 2026
Behavioral round with the hiring manager for an ML Engineer role at Apple. The focus was less on coding and more on how you think, communicate, and handle ambiguity, which I wasn't fully expecting going in.
  • Walk me through two projects from your resume in depth.
  • What experience do you have working with AI agents?
  • How do you collaborate with designers on your team?
  • How do you negotiate with stakeholders to define and scope a project?

“I picked projects I thought were impressive but hadn't practiced talking through out loud.”

View Post