Apple’s interviews are run largely by the hiring team, so the exact format depends on the role: an iOS app engineer, a kernel engineer and a cloud services engineer will not get the same loop. What carries across teams is a coding interview on data structures and algorithms, questions about the technology the team works on, and a lot of conversation about your past work. Prepare the fundamentals first, then study the specifics of the team in the job posting.
Apple does not publish an official interview guide. What follows combines what candidates and prep sites report, as of October 2026, with preparation advice. Details such as the number of rounds change, so ask your recruiter what to expect.
What the process usually looks like
- Recruiter conversation of 20 to 30 minutes about your background and the role. You may not hear much about the specific team at this point.
- One or two technical phone or video screens of 45 to 60 minutes with engineers from the team, typically with live coding.
- A long on-site or virtual loop of several interviews, often with different engineers and a manager. Candidates report anywhere from four to eight conversations of 30 to 60 minutes, and that interviewers can be quick and interrupt with follow-ups.
- Team-by-team decisions. Teams choose their own questions and weigh feedback themselves, so two candidates for different roles can have very different experiences.
Total time from application to offer is commonly reported at one to two months, but it varies widely. Do not read much into silence.
What the coding rounds look like
Expect medium-difficulty problems on arrays, strings, hash maps, linked lists, trees, graphs, sorting, recursion and sometimes dynamic programming. Interviewers care about a working, readable solution, clear reasoning about complexity, and whether you handle edge cases without prompting. A problem often starts simple and gains requirements as you solve it.
Here are two representative problems with solutions tested in Python.
Longest palindromic substring
Brute force checks every substring, which is O(n^3). A palindrome mirrors around its center, so expand outward from each of the 2n – 1 possible centers: O(n^2) time and O(1) extra space. Dynamic programming is also O(n^2) but needs O(n^2) space, so expanding around the center is the better answer here.
def longest_palindrome(s):
if not s:
return ""
start, best = 0, 1
def expand(left, right):
while left >= 0 and right < len(s) and s[left] == s[right]:
left -= 1
right += 1
return right - left - 1 # length of the palindrome found
for i in range(len(s)):
n = max(expand(i, i), expand(i, i + 1)) # odd and even centers
if n > best:
best = n
start = i - (n - 1) // 2
return s[start:start + best]
print(longest_palindrome("babad")) # bab
print(longest_palindrome("cbbd")) # bb
print(repr(longest_palindrome(""))) # ''For “babad”, “aba” is equally valid, so say so if asked: the problem allows any longest answer. The Palindrome Substrings lesson uses the same expand-from-center idea.
Group anagrams
Words are anagrams when their sorted letters match, so use the sorted word as a dictionary key. That is O(n * k log k) for n words of length k.
from collections import defaultdict
def group_anagrams(words):
groups = defaultdict(list)
for w in words:
groups["".join(sorted(w))].append(w)
return list(groups.values())
print(group_anagrams(["eat", "tea", "tan", "ate", "nat", "bat"]))
# [['eat', 'tea', 'ate'], ['tan', 'nat'], ['bat']]A follow-up is to avoid sorting by using a 26-count tuple as the key, which gives O(n * k). You can try this on the Group Anagrams lesson.
Language choice
Use the language you are fastest in for the algorithm questions. Apple’s teams use Swift, Objective-C, C, C++ and others, depending on the platform, and some will ask you to code in the language the team uses, especially for platform roles. Read the posting for hints. If you apply for an iOS or macOS role, practice solving problems in Swift, because syntax slips cost time.
One Swift detail that comes up in string questions: a Swift String is a collection of Character values (extended grapheme clusters), not bytes or code units. You cannot index it with an integer, count is not O(1), and reversing a string reverses characters, not the individual code points. Convert to an Array when you need random access, and say so while you do it.
Platform and systems knowledge by role
- iOS and macOS apps: value types versus reference types (structs and classes), automatic reference counting and retain cycles, closures capturing
self, optionals, the main thread and what must not block it, concurrency with Grand Central Dispatch and Swift concurrency (async/await, actors), and the app life cycle. - Systems and performance: C or C++ fluency, pointers and memory layout, threads, locks and deadlocks, virtual memory, caches, file systems.
- Services and infrastructure: API design, databases and indexing, caching, queues, consistency, observability. Practice questions such as designing file sync between devices, a notification service or a photo-storage backend.
- Machine learning and data: the basics of your area plus the usual coding round.
Use Apple’s own developer documentation (developer.apple.com) for the platform part. It is free and what the teams themselves write against.
Design and object-oriented questions
For experienced candidates, expect either object-oriented design (model a parking lot, a music library, a calendar) or system design. Use the same frame every time: clarify requirements, list the core entities or components, sketch the interfaces, walk through one main flow, then discuss scale, failure and trade-offs. The article on object-oriented design in coding interviews shows the steps.
Behavioral questions
Apple teams often care about craftsmanship, collaboration across disciplines and care for the user. Prepare real stories rather than slogans: a bug you chased to its root, a time you pushed back on a decision with evidence, a feature you cut to ship on time, a detail in a product you cared about that nobody else noticed. A structure that works is situation, your action, result, and what you learned. Choose stories whose details you can answer questions about, because follow-ups are the norm.
A four-week preparation plan
- Week 1: arrays, strings, hash maps, two pointers, sorting. Solve 15 to 20 problems; re-solve the ones you missed.
- Week 2: linked lists, trees, graphs, recursion, BFS and DFS. State complexity on every one.
- Week 3: your platform specifics and one design question per day, spoken aloud.
- Week 4: timed mock interviews with another person, two behavioral stories rehearsed, and questions prepared for your interviewers.
For problems with step-by-step guidance in Python, JavaScript, Java or C++, AlgoCademy is one option; LeetCode and HackerRank are the other common choices.
During and after the interview
- Clarify the problem, give a simple solution, then improve it, and think aloud throughout.
- Test with a normal case and an edge case before saying you are done.
- If you are stuck, say what you have tried and ask a specific question; interviewers prefer that to silence.
- Afterward, write down every question you were asked while you remember it. It makes the next round, here or elsewhere, easier. See the guide on following up after a coding interview.
