Adobe interview experiences from US new grads and India MTS hires

Adobe’s interview process for US interns and new grads in 2025 reports is a HackerRank test of about three LeetCode medium problems, one live coding interview with an engineer (a medium priority queue problem in one October 2025 loop), and a behavioral interview with the hiring manager. Some teams swap in low-level API design or reading a Java snippet aloud. Senior candidates start with a hiring manager call or a HackerRank test; one 2025 San Jose onsite had two LeetCode mediums and two system design rounds.

Adobe’s 2026 University Graduate - Software Engineer posting lists a base range of $76,700-$152,200, or $105,100-$152,200 in California and New York. India campus and off-campus MTS loops in Noida and Bangalore run longer, and their reports are grouped near the end of the page.

Adobe interview process, round by round

Adobe interview process reports from US interns and new grads, 2025

US new grad write-ups for Adobe are scarce, so this section adds 2025 US intern and early-career reports. They come from LeetCode Discuss and Taro and are short.

• Salt Lake City intern and new grad loop, offer. A LeetCode Discuss author who interviewed in November 2024 for the Summer 2025 internship (and later the Summer 2026 new grad role) describes three LeetCode medium problems on HackerRank, a live problem-solving interview and a behavioral interview with the hiring manager, then an offer call.
• US intern, October 2025, no offer. An online assessment of LeetCode classic problems, then an interview with an engineer built around a medium priority queue problem, plus standard behavioral questions. The candidate called the process smooth.
• Software development engineer, October 2025, no offer. A 30-minute phone call with the hiring manager, then two hours of in-person interviews with team members: API design, simple coding on a whiteboard, past work, and reading a Java snippet to explain what it does and how to improve it.
• Lehi software engineer, March 2025, no offer. A team call before the loop, then three rounds: one hard LeetCode problem, a behavioral round and a low-level system design round on APIs and error messages.
• San Jose software engineer, August 2025, accepted offer. A conversational process focused on the candidate’s previous research and projects, followed by a check on skills and fit with Adobe’s values.
• San Jose SDE, February 2025, no offer. The hiring manager reached out about two months after the application. A 30-minute call with the manager, then a live round with two LeetCode mediums ending in a request to mock an API call with pagination; the candidate ran short of time. Asked: “Where do you see yourself in the next 5 years?”
• San Jose senior software engineer, February 2025, no offer. HackerRank test, then an onsite with a coding round of two LeetCode mediums, a manager round and two system design rounds. The recruiter stopped responding after the onsite.
• San Jose senior software engineer, March 2025, no offer. A 50-minute phone call with the hiring manager to check fit, including the candidate’s view on code tools such as Cursor and “What is the biggest challenge in my work?”

What the US reports share: the coding bar is LeetCode medium with little slack on time, the hiring manager is involved early, and the format depends on the team more than on a company-wide script.

Adobe interview questions that come up often

Coding / technical

1. Longest palindromic substring - expand around center
2. Minimum window substring - sliding window + hashmap
3. LRU / LFU cache - hashmap + doubly linked list
4. Serialize and deserialize a binary tree
5. Trapping rain water - two-pointer
6. Word ladder - BFS shortest transformation
7. Course schedule I/II - topological sort
8. Merge k sorted lists - min-heap
9. Word Break II - backtracking with memoization
10. Special Binary String - recursive split and lexicographic merge (reported hard)
11. String to Integer (atoi) - overflow and whitespace edge cases
12. Output-prediction MCQs - pointers, static vars, precedence (OA)

System design (lightweight, fresher-depth)

1. Design a basic document-storage or file system
2. Design a URL shortener
3. Design a rate limiter
4. Design a notification system

Behavioral

1. Why Adobe?
2. Tell me about a challenging project and what you learned
3. How do you handle disagreements in a team?
4. Walk me through a design decision you’d change today, and why
5. Where do you see yourself in five years?

Pre-interview checklist (Adobe SDE/MTS)

• Read your test email for platform, round count, and location; US tests are on HackerRank.
• OA: split prep - 2 timed coding problems plus 10–20 output-prediction / OS / DBMS MCQs per day.
• Live rounds: practice thinking aloud before typing.
• Resume: one project deep-dive with decisions, alternatives, and what you would change today (Director round).
• Prepare 3 STAR stories - ownership, disagreement, feedback - aligned with Adobe values language.
• Behavioral/HM: research Check-in culture and three business segments beyond “Photoshop company.”

Adobe interview tips from candidate reports

• Practice two LeetCode mediums in 45 minutes with a follow-up; the February 2025 San Jose SDE candidate ran out of time on exactly that.
• Know your resume project cold - the Hiring Manager/Director round goes deep on the reasoning behind specific design choices, not just the result.
• Prepare a small API design (endpoints, pagination, error messages); it came up in Lehi, San Jose and an October 2025 in-person loop.
• Revise OS/DBMS/networking fundamentals; they show up as standalone questions, not just inside coding problems.

Behavioral interview

In US loops the hiring manager carries most of the behavioral weight, sometimes as the first call (30 minutes in two 2025 reports, 50 minutes for a senior candidate). Reported prompts: where you see yourself in five years, the biggest challenge in your work, your previous research or projects, and how you use AI code tools. The Adobe behavioral interview questions page has STAR sample answers for these.

Sample answers for high-frequency questions

Q. How do you design an LRU cache with O(1) get and put?
A. Combine a hash map with a doubly linked list. The hash map maps each key to the address of its node, giving O(1) lookup; the doubly linked list keeps nodes in recency order with the most recently used at the head and the least recently used at the tail. On get, look the key up in the map, unlink its node and re-insert it at the head, and return the value. On put, if the key exists update the value and move the node to the head; otherwise create a node at the head, and if the size now exceeds capacity remove the tail node and delete its key from the map. A doubly linked list is essential because unlinking a node given only a pointer to it needs both neighbours, which is O(1) here and O(n) with a singly linked list. Both operations are O(1) time and the structure is O(capacity) space. LFU differs by keying on frequency: you keep a map from frequency to a list of keys plus a minFreq pointer to still get O(1).

Q. Explain the two-pointer solution to Trapping Rain Water.
A. Water above index i equals min(maxLeft[i], maxRight[i]) - height[i], where maxLeft and maxRight are the tallest bars to the left and right. The naive approach precomputes both arrays in O(n) time and O(n) space. The two-pointer version removes the arrays: keep pointers left at 0 and right at n-1 plus running leftMax and rightMax. At each step, if height[left] < height[right] then leftMax is guaranteed to be the binding constraint for the left pointer, because some bar at least as tall as height[right] exists on the right; so add leftMax - height[left] to the answer and advance left, otherwise do the symmetric thing on the right. Each pointer moves at most n steps, so the algorithm is O(n) time and O(1) extra space. Adobe interviewers usually ask you to justify why the smaller side is safe to settle first.

Q. How do you find the longest palindromic substring efficiently?
A. The expand-around-center approach treats every one of the 2n-1 possible centers, n single characters and n-1 gaps between characters, and expands outward while the characters on both sides match, tracking the longest span found. That is O(n squared) time and O(1) space, and it is the answer most interviewers expect. The dynamic-programming formulation, where dp[i][j] is true when s[i] == s[j] and dp[i+1][j-1] is true, is also O(n squared) time but costs O(n squared) space, so it is strictly worse in practice. If pushed for optimal, Manacher’s algorithm achieves O(n) by inserting separator characters to make every palindrome odd-length and reusing previously computed radii mirrored about the current center, so each position is expanded past only once.

Q. How does topological sort solve Course Schedule II, and how do you detect a cycle?
A. Model courses as vertices and each prerequisite pair as a directed edge from prerequisite to course, then output any topological order. Kahn’s algorithm computes the in-degree of every vertex, pushes all zero in-degree vertices onto a queue, and repeatedly pops a vertex, appends it to the result, and decrements the in-degree of each neighbour, enqueueing any that hit zero. If the result contains fewer than V vertices when the queue empties, the remaining vertices sit on a cycle and no valid ordering exists, which is exactly the Course Schedule I answer. The DFS alternative colors vertices white, grey, and black; encountering a grey vertex means a back edge and therefore a cycle, and pushing vertices onto a stack at post-order gives the reverse topological order. Both run in O(V + E) time and O(V + E) space.

Q. Why do relational databases use B+ trees for indexes rather than hash indexes or binary search trees?
A. A B+ tree is a balanced multi-way tree where internal nodes hold only keys for routing and all actual records or row pointers live in the leaves, which are chained in a linked list. Node size is matched to the disk page, typically 4 to 16 KB, so a node holds hundreds of keys and the tree stays about 3 to 4 levels deep even for hundreds of millions of rows, meaning a lookup costs only a handful of page reads. A binary search tree has fan-out 2, so its depth would be around 27 for 100 million rows and every level is a potential disk seek. A hash index gives O(1) equality lookups but cannot serve range scans or ORDER BY, whereas the B+ tree’s linked leaf level makes range queries a sequential walk. Adobe follow-ups usually cover clustered versus secondary indexes and why a leading-column mismatch stops a composite index from being used.

Adobe online assessment and interview tips

Adobe India: MTS interview experiences from Noida and Bengaluru

India campus and off-campus cycles in Noida and Bangalore run longer than US loops: an online test with aptitude, core CS MCQs and coding, two technical rounds, a Hiring Manager or Director round and a short HR close. Reported total pay for entry-level SDE/MTS roles in India spans roughly ₹20-45 LPA (base plus stock/bonus), depending on college tier, CGPA and business unit. India tests are mostly on HackerRank, though some campuses have used CoCubes. For OA drills, see Adobe on placementpapers.app.

Real interview experiences from India

Experience 1: MTS-1, on-campus (GeeksforGeeks Set 52)

Source: GeeksforGeeks, Adobe Set 52 (campus MTS-1)

Profile: On-campus MTS-1 candidate; Adobe ran separate aptitude and coding OAs on HackerRank (50 and 90 minutes).

Online aptitude (45 MCQs) plus three coding problems: classify a string with '?' wildcards as GOOD, BAD or MIXED (rules on consonant and vowel runs), a volume-change DP over a list of songs, and a minimum-changes problem so no two adjacent values from {1,2,3,4} are equal.

Technical 1 (40 min): a relative-velocity physics question, coursework, OS and DBMS, then count ways to reach the nth stair and level-order traversal.

Technical 2 (40 min): projects; CPU scheduling, multilevel queues, mutexes and deadlocks; left, right and top views of a binary tree; a binary search problem; a sorted character stream with membership queries; how to allow only one instance of an application and how to stop a class being allocated on the heap.

Technical 3 (90 min): project deep-dive, supervised vs unsupervised ML, a system design question for Android and web apps, caching strategies, and an LRU cache with a hash map and doubly linked list, then why Adobe.

HR (20 min) and rapid fire (25 min): motivation, ethical scenarios and college life; then quick coding, OS theory, and what happens at the OS level when a program calling fopen is compiled and run.

Result: about 15 shortlisted for interviews, four reached HR, three were selected. The author was not selected.

Takeaways

• What decided it: older campus sets show Adobe mixing creative string/DP puzzles with standard DSA and OS depth, so practice both, not only LeetCode top 100.

Experience 2: SDE backend, five-round loop (August 2026)

Source: DevBrainiac, Adobe SDE interview, Aug 2026

Profile: Backend SDE-1 applicant; public blog report of five rounds.

Round 1: Aptitude plus 20 CS MCQs (DSA basics, OS processes/threads/memory, DBMS normalization/joins/indexing, SQL optimization).

Round 2: Two DSA problems in 60 minutes: rotate an array by k (brute force, then in-place reversal) and merge all elements of two BSTs into a sorted list, with follow-ups on unbalanced trees and a lazy iterator.

Round 3: One short coding problem, then a conversation about hobbies, keeping up with tech trends, and balancing work with learning.

Round 4: Kth smallest in a sorted matrix (min-heap, then binary search on the value), plus a code review of small snippets: == vs .equals() on Integer, an off-by-one loop bound, String += in a loop.

Round 5: Behavioral, no coding: past work, why Adobe, working style.

Result: selected.

Takeaways

• What decided it: even when coding count is high, MCQ fundamentals in round 1 eliminate candidates who only grind problems.

Experience 3: Campus pool drive at IGDTUW, Noida/Bangalore SDE (LinkedIn)

Source: Nidhi Sharma on LinkedIn, Adobe interview experience

Profile: October pool drive at IGDTUW for SDE (Noida or Bangalore); open to B.Tech CSE/IT/ECE and some M.Tech branches, with 70% in graduation and 60% in Class 10 and 12 and no current backlogs.

OA platform: CoCubes, webcam on: 30-minute aptitude + 30-minute two-problem coding.

Interviews: three technical interviews + HR (five rounds total with the OA).

Reported topics: reverse a linked list in groups of k, clone a list with random pointers, zig-zag traversal, activity selection, check if a tree is a BST, missing element, pair with given sum, HashMap internals, overriding hashCode(), OS memory, paging, deadlocks and scheduling, DBMS keys, SQL and normalization. Interviewers wanted C++ or Java code with dry runs.

Takeaways

• What decided it: confirm platform and round count in your OA email - Adobe India uses both HackerRank and third-party OAs depending on campus year.

Sources

US reports: LeetCode Discuss: Adobe US intern and new grad loop, Taro: US intern, October 2025, Taro: software development engineer, October 2025, Taro: Lehi, March 2025, Taro: San Jose, August 2025, Taro: San Jose SDE, February 2025, Taro: San Jose senior, February 2025, Taro: San Jose senior, March 2025, and Adobe’s 2026 University Graduate - Software Engineer posting.

India reports: Adobe Interview Experience Set 52 (MTS-1) - GeeksforGeeks, Adobe SDE five-round experience - DevBrainiac (2026), Adobe India campus drive reports - LinkedIn (IGDTUW pool).

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