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Apple interview notes, page 23 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·Data Scientist·Technical Phone Screen

Jun 2026
Apple DS interview with a meaty A/B testing design question. One question, but it had a lot of moving parts and they clearly wanted you to think through the whole thing, not just recite a formula.
  • Walk through how you would design an experiment to measure the impact of a new homepage layout on purchase rate, covering population definition, metrics, randomization strategy, sample size calculation, test duration, bias control, and analysis plan.

“This one sprawled.”

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Apple·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Apple data engineering interview focused on large-scale system design. The main problem was building a pipeline to handle massive video upload traffic, with streaming and scalability at the center of everything.
  • Design a pipeline that can handle a massive volume of concurrent video upload events, with a focus on streaming processing and horizontal scalability.

“I went straight to Kafka as the ingestion layer and Flink for stream processing, which felt right, but I spent too long on the happy path and barely touched failure scenarios until they pushed me.”

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Apple·Machine Learning Engineer·Onsite - Coding / Algorithms

Jun 2026
Apple ML engineer coding round, vision-focused. Three problems covering classic algo territory and some computer vision specifics. Nothing too wild but the image retrieval question had a scaling follow-up that caught me a bit flat-footed.
  • Given an array of distinct integers, return all possible permutations.
  • Implement a grayscale image filter using convolution with a fixed 3x3 kernel on a 2D integer matrix. Explain how you handle boundary cells.
  • Given a query image embedding and a gallery of embeddings, return the index of the most similar image. You can use cosine similarity or Euclidean distance. Explain the complexity of your approach and how you'd scale it past a brute-force scan.

“Classic backtracking.”

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Apple·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Apple ML Engineer interview that went deep on GPU memory management. The whole session felt like one long debugging scenario, which I wasn't fully expecting. Came out of it with a lot to think about.
  • How would you diagnose a GPU out-of-memory error during model training? Walk through your process.
  • What are the main sources of memory pressure during deep learning training, and how do you identify which one is causing your OOM?
  • Compare gradient checkpointing and mixed-precision training as memory optimization strategies. When would you choose one over the other?
  • How do ZeRO, FSDP, and model parallelism differ in how they address memory constraints across multiple GPUs?
  • At inference time, how do you manage memory for large language models? What techniques exist and what are their tradeoffs?
  • If you had to choose between throughput, memory efficiency, and model accuracy when training under a tight memory budget, how do you reason about that tradeoff?

“Started with nvidia-smi which is always my reflex, but they pushed me to go further.”

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

Jun 2026
Apple MLE system design round focused entirely on building search for a mobile app marketplace. Dense question with a lot of surface area to cover, and I felt the time pressure pretty hard.
  • Design a search system for a mobile app marketplace where users type short queries like 'photo editor' or an exact app name, and the system needs to return relevant results with low latency.

“This question has so many sub-components that I burned the first few minutes just trying to figure out where to start.”

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Apple·Data Scientist·Technical Phone Screen

Jun 2026
Apple Data Scientist technical screen focused entirely on implementing a SparseVector class from scratch, covering dot product and cosine similarity. Pretty algorithmic for a DS role, felt more like a software engineering interview than anything else.
  • Implement a SparseVector class that stores high-dimensional vectors efficiently, and write a dot product method that works correctly and efficiently when most entries are zero.
  • Extend the SparseVector class with a cosine similarity method, and explain how you handle edge cases like zero-norm vectors.

“The core trick is iterating over the smaller of the two non-zero maps instead of looping over all dimensions.”

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Apple·Data Scientist·Take-home Assignment

Jun 2026
Apple data scientist take-home that was basically a full ML system design exercise disguised as a coding prompt. One question, but it covered everything from feature engineering to post-deployment monitoring, so budget your time accordingly.
  • Build an end-to-end scikit-learn pipeline to predict 28-day churn using only features available at decision time, handling high-cardinality categoricals, class imbalance, temporal leakage, hyperparameter tuning, probability calibration, and post-deployment drift monitoring.

“This is a lot to fit into one question.”

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Apple·Software Engineer·Technical Phone Screen

Jun 2026
Apple SWE interview with a toll calculation problem that looked straightforward until you actually sat down to think through all the edge cases. Not a bad experience but the problem had more moving parts than I expected.
  • Given a list of unsorted log entries in the form (license plate, checkpoint, timestamp), compute the total toll owed by each vehicle. Each trip segment between consecutive checkpoints for the same vehicle costs a base fee of $2 plus $0.10 times the Euclidean distance between the two checkpoints. A helper function gives you the coordinates of any checkpoint. Return a map from plate to total toll.

“My first instinct was just to sort everything and iterate, which is basically right, but I fumbled the grouping step initially.”

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Apple·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Apple for a software engineer role. Two questions, both pretty standard but they expect real depth on the impact side, not just a story.
  • What project are you most proud of? Walk through the goal, what you personally owned, and the outcome.
  • Describe the hardest moment you faced on a project, technical or otherwise. What made it difficult, what did you do, and what did you take away from it?

“I had a solid project ready but fumbled explaining my specific ownership versus the team's.”

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Apple·Software Engineer·Hiring Manager Screen

Jun 2026
Interviewed for a business analyst role at Apple and got hit with a classic market sizing question. Nothing too wild but it made me realize how much structure matters when you're estimating on the fly.
  • How would you estimate the total addressable market (TAM) for a new product?

“I went top-down first, which felt safe, but then they pushed me to try it bottom-up and I stumbled a bit.”

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