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    Apple

    FAANG / Big Tech

    Apple is a multinational technology company that designs, manufactures, and sells consumer electronics, software, and services, including the iPhone, iPad, Mac, Apple Watch, and services like iCloud and the App Store. It is one of the world's most valuable companies, renowned for its integrated hardware-software ecosystem and premium product design.

    368total notesUpdated 11 days ago

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    Browse all 368 notes for Apple. Each answer is trimmed to its first line — create a free account to read them in full.

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    Apple·Software Engineer·Onsite - Multi Round
    Pending
    Aug 2026

    Got my team match for Apple's IS&T org and now prepping for a three-round SRE panel: a hiring manager behavioral screen, a technical coderpad round, and a shorter early career community behavioral chat. The req emphasizes Linux, Python automation, incident response, and troubleshooting, but I'm still trying to figure out whether the coding round leans LeetCode-style DSA or more scripting and ops problems.

    What is the coderpad technical round actually like for this role? Is it standard algorithm problems or does it lean toward scripting and automation given the Python and Linux focus?How much do the technical questions focus on incident response and troubleshooting versus general software engineering concepts?

    “This is the thing keeping me up.”

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    Apple·Data Scientist·Online Assessment (OA)
    Jul 2026

    Apple DS interview that was basically three coding tasks back to back, SQL and Python both fair game. Nothing too wild but the third problem had me second-guessing my greedy approach the whole time.

    Given an employees table with salaries, return the second highest distinct salary, or NULL if one doesn't exist.You have a users table and a user_events table. Merge them so each event row includes user attributes, keeping only events with a matching user.Given a list of employees with salaries and a total budget B, find the maximum number of employees you can hire without exceeding the budget.

    “Pretty classic, but the NULL edge case is where people trip up.”

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    Apple·Data Scientist·Technical Phone Screen
    Jul 2026

    Apple Data Scientist technical screen, one coding question centered on sliding window stuff. Pretty straightforward session, felt more like a warm-up than a deep technical grilling.

    You have an array of positive integers where each value represents website hits in a given minute. Given a window size k, find the maximum total hits across any contiguous window of length k. What's the time and space complexity of your solution?

    “Knew the sliding window trick going in, so I coded it up pretty fast.”

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    Apple·Software Engineer·Technical Phone Screen
    Jul 2026

    Got a coding question for an Analytics Engineer role at Apple that was more Python OOP than I expected. The problem wasn't crazy hard but the constraints tripped me up a bit.

    Given a Parent class that precomputes a frequency dictionary from a list, implement a `most_frequent_key()` method in a Child subclass that returns all keys tied for the highest frequency, in order of first appearance, without using `max()` or `collections.Counter`.

    “The no-`max()` constraint is what got me thinking for a second.”

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    Apple·Machine Learning Engineer·Technical Phone Screen
    Jul 2026

    Apple ML engineer interview with a coding problem centered on implementing a TF-IDF scorer from scratch. The problem looked like a search relevance exercise but had enough edge cases in the math to keep things interesting.

    Implement a simplified TF-IDF scorer: given a list of documents and a query string, compute a score for each document using term frequency and smoothed inverse document frequency, then return the document indices sorted by descending score, breaking ties by smaller index.

    “The formula they gave had that +1 smoothing on both numerator and denominator of the IDF, plus an extra +1 added to the whole log.”

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    Apple·Data Scientist·Technical Phone Screen
    Jul 2026

    Apple data scientist interview, technical phone screen focused on ML pipelines. One big open-ended question about building a churn model end-to-end in scikit-learn. Felt more like a design conversation than a coding test, which I wasn't fully prepared for.

    Walk through how you'd build a churn-prediction pipeline from scratch in scikit-learn, covering preprocessing, model selection, hyperparameter tuning, and packaging it for production.

    “This was basically the whole interview.”

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    Apple·Machine Learning Engineer·Onsite - System Design / Architecture
    Jul 2026

    Apple ML engineer interview, system design round focused on on-device image processing. The question was dense and covered a lot of ground, felt like they were testing whether you could actually ship something constrained rather than just talk theory.

    You're building an image filter feature that runs entirely on-device. Users expect preview latency under 30ms, low memory overhead, and minimal battery drain. Walk through how you'd optimize the filter pipeline without sacrificing visual quality, covering algorithm selection, numerical precision, CPU vs GPU vs accelerator usage, memory layout, tiling or streaming strategies, thermal constraints, and quality measurement.

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

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    Apple·Data Scientist·Recruiter / HR Screen
    Jul 2026

    Phone behavioral screen for a Data Scientist role at Apple. Pretty short call, just one question but it required some real prep to not sound generic.

    Why do you want to work at Apple, and which of Apple's values connect with your past work?

    “I had a decent answer prepared but partway through I realized I was just listing values without actually tying them to anything real.”

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    Apple·Data Scientist·Technical Phone Screen
    Jul 2026

    Apple DS interview with a product analytics case around a checkout conversion drop. The question was meaty and covered a lot of ground, felt like they wanted to see if you'd actually think through the noise vs. signal problem before jumping to conclusions.

    A new feature was released and checkout conversion appears to have dropped. How would you determine whether this is a genuine regression or just random noise? Walk through the metrics, segmentation approaches, and any experimental or quasi-experimental methods you'd apply.

    “I started with the time-series angle, basically asking whether the drop is outside normal variance before doing anything else.”

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    Apple·Machine Learning Engineer·Technical Phone Screen
    Jul 2026

    Apple ML engineer screen, four questions all in the fundamentals-of-large-models territory. Nothing tricky or weird, but the inference optimization question had more depth to it than I expected going in.

    What is a vision encoder and what role does it play in a computer vision or multimodal model?How is a vision encoder typically trained?What are the main performance bottlenecks of large language models during inference?+1 more

    “Pretty foundational, I talked through how it maps raw pixel data into a dense embedding space that downstream components can actually reason over.”

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