Adobe

Large Enterprises

Most of these Adobe write-ups come from Software Engineers (30), then Machine Learning Engineers (15) and Product Managers (8). The most reported rounds are the technical phone screen (32), the system design round (8), the product sense round (7) and the multi-round onsite (4).

58 interview notes · updated Sep 2026

Adobe·Software Engineer·Onsite - Multi Round

Sep 2026

This is a pre-interview crowdsourcing post from someone trying to gather intel on Adobe's Software Engineer Intern interview before candidates go in. No personal experience shared yet, just a breakdown of what they're hoping to learn across the three rounds: code review, bug solving, and a final technical discussion.

In the code review round, is the candidate given a single file or a full repository to navigate through?
How much context or prompting is provided before the code review begins?
In the code review round, is the expectation a high-level summary of the code or specific line-by-line feedback?
In the bug solving round, is the candidate working with a single file or navigating a larger repository?
Are the bugs in the bug solving round algorithmic in nature or more system and syntax related?
During the bug solving round, are candidates expected or allowed to use AI tools, and do they need to run existing test cases?
In the final round, does the interviewer focus on past assessment submissions, your resume, or general technical questions?

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Adobe·Software Engineer·Onsite - Multi Round

Aug 2026

Interviewed at Adobe Noida for an MTS-2 role and got knocked out in the LLD round after barely surviving a brutal DSA round. Two rounds total, both harder than expected in different ways, ended in rejection.

Solve a dynamic programming problem on a tree structure.
Solve a matrix-based algorithmic problem under strict time pressure.
Design a food court system, then extend it with additional constraints provided by the interviewer.

“Got maybe 60-70% of test cases passing and ran out of time before fixing the rest.”

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Adobe·Data Scientist·Recruiter / HR Screen

Jul 2026

Fit and background chat with HR and a director for a Data Scientist role at Adobe. Pretty standard stuff, nothing too curveball-y, but the timeline question caught me a little flat-footed.

Walk us through your background and explain why it's a good fit for this role.
Tell me about a time you had to deliver something under a very tight deadline. What did you do and what was the result?

“I rambled a bit here.”

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Adobe·Software Engineer·Technical Phone Screen

Jul 2026

Adobe SWE interview that leaned heavily on systems thinking. The main question was a deep dive into word frequency counting at scale, and it covered a lot of ground fast.

Write a function to compute word frequencies from a very large text file. Cover how you'd tokenize and normalize the input, handle files bigger than available RAM, and efficiently find the top-K most frequent words. Walk through time and space complexity.

“This one sprawled in a way I didn't expect.”

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Adobe·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Adobe ML Engineer screen that basically turned into a deep dive on data quality. One main technical question but it had a lot of branches and I wasn't fully prepared for how far they'd push it.

Walk me through how you handle missing data in a machine learning pipeline, from identifying why it's missing all the way through choosing an imputation strategy.

“Started okay by bringing up MCAR vs MAR vs MNAR, which seemed to land well.”

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Adobe·Software Engineer·Technical Phone Screen

Jul 2026

Adobe SWE interview with a dynamic programming problem that looks straightforward until you start thinking about the bonus condition. The question had enough moving parts to trip you up if you're not careful with your state transitions.

You're given a binary string representing a work schedule, where '1' is a workday and '0' is a rest day. You also have a base daily pay, a bonus earned whenever two consecutive days are both workdays, and a budget of k flips to turn rest days into workdays. What's the maximum total pay you can achieve, and what's the time and space complexity of your approach?

“I went straight to greedy and immediately realized that was wrong because flipping a single zero can affect two potential bonuses depending on its neighbors.”

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Adobe·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Adobe MLE interview focused heavily on distributed training infrastructure. Four questions, all technical, no fluff. The kind of round where you either know your stuff or you don't.

How do you track hardware FLOP utilization (HFU) during a distributed training run, and what does a low HFU number tell you?
Where do communication bottlenecks typically show up in distributed training, and how would you go about diagnosing one?
Walk me through your approach to GPU profiling when you're trying to find inefficiencies in a training job.
What techniques have you used to improve training throughput, and how do you decide which one to apply first?

“This tripped me up more than I expected.”

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Adobe·Software Engineer·Technical Phone Screen

Jun 2026

Adobe full-stack round, one coding question the whole time. They gave me a Node.js filesystem problem and just let me run with it, which I wasn't expecting to be as tricky as it turned out.

Using Node.js, write a function that takes a file path and recursively reads all files and subdirectories under it, then prints the directory structure as an indented tree.

“I went straight for the sync version with readdirSync and statSync because I didn't want to fumble async error handling under pressure.”

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Adobe·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Adobe ML engineer interview that was pretty much a loss function quiz. Three questions, all theory-heavy, felt like a written exam more than a conversation.

What loss function is used in logistic regression, and what's the reasoning behind that choice?
What loss function does linear regression use, and why is it the standard choice?
Compare MSE and MAE across their mathematical forms, how they handle outliers, gradient behavior, and when you'd pick one over the other.

“Binary cross-entropy, and the 'why' is the part people fumble.”

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Adobe·Software Engineer·Technical Phone Screen

Jun 2026

Adobe SWE interview that went deep on a file processing problem. The question started simple but kept expanding into memory constraints and data structure trade-offs, which I wasn't fully ready for.

Write a function to read a large text file and return the frequency count of each word. You'll need to define your tokenization and normalization strategy, handle memory constraints through streaming or chunking, and return the top-k most frequent words. Also analyze time and space complexity and compare hash maps, tries, and external sorting when the file barely fits in memory.

“This one kept growing legs.”

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Adobe interview FAQ

What rounds are in the Adobe interview?

Candidates most often report a technical phone screen (32 write-ups), a system design round (8), a product sense round (7) and a multi-round onsite (4).

What does the Adobe technical phone screen cover?

Across 32 technical phone screen write-ups, the most tagged question topics are Technical Trade-offs (53 questions), Algorithms & Data Structures (39) and System Design (26).