Adobe ML Engineer candidates describe 2-4 interviews that mix ML fundamentals, project deep dives, and coding. One senior candidate faced a tree traversal data structures round, and one intern was asked about generative AI.
$190K
Avg. Base Comp
$234K
Avg. Total Comp
2-4 rounds
Typical Rounds
Not reported
Process Length
Three candidates describe Adobe's ML Engineer interviews, and their experiences differ by level. A senior candidate reports four interviews: three technical and one with the hiring manager. That candidate did not get an offer. Two intern candidates each report two rounds and both received offers.
The clearest coding signal comes from the senior candidate. They describe a data structures round framed around web crawling for an e-commerce site, where the interviewer wanted a tree traversal implemented from scratch and explained. They found it closer to a general software engineering screen than an ML check, so practice writing core traversal code by hand. One intern reports a simple coding problem, incrementing a large number, followed by questions on logistic regression, stochastic versus batch gradient descent, and basic classification. The other intern says their process had no coding portion.
Project depth also comes up. One intern describes a resume walkthrough of their ML projects, then a case study on designing an ML pipeline covering preprocessing, model selection, and evaluation. The other intern describes a team round on generative AI and recent papers, followed by a manager conversation about past projects and fit.
In practice, prepare on three fronts: DSA fundamentals, ML basics you can explain in your own words, and your own projects with their trade-offs. If you are interviewing for a team working on generative AI, read recent papers relevant to that area.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Adobe process.
I'll be blunt about this one: I went through four interviews at Adobe for an ML role, three technical and one with the hiring manager, and didn't get an offer. The round that stuck with me was a data structures round, which I didn't expect for a machine learning position. The problem was framed around web crawling for an e-commerce site, which sounds practical enough, but the interviewer really wanted me to implement a tree traversal algorithm and walk through how it worked. Coming from an ML background, I found that jarring. It felt more like a general software engineering screen than a check on how I'd build or evaluate models. I'd rather be honest about that than pretend the process was well run. If you're interviewing here, don't assume the coding bar will match the title, and be ready to write traversal code from scratch.
Prep tip from this candidate
Be ready to implement tree traversal from scratch in a data structures round, even for an ML role, and expect it to be framed around a practical scenario like crawling a website. Don't assume the technical bar will be limited to ML topics.
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| Question | |
|---|---|
| Bank Fraud Model | |
| Weekly Aggregation | |
| Hurdles In Data Projects | |
| Real-Time Transaction Streaming | |
| Z and t-Tests | |
| RAG Strict Source Control | |
| Replace Words with Stems | |
| Data Preparation for Imbalanced Data | |
| Overfit Avoidance | |
| Decreasing Subsequent Values | |
| Shortest Path Algorithms | |
| Text Editor With OOP | |
| The Longest Journey | |
| Ticket Reservation Locking | |
| Confidence Interval Explanation | |
| Proof k-Means Converges | |
| Merge Sorted Lists | |
| Subscription Overlap | |
| String Shift | |
| Find the Missing Number | |
| Bagging vs Boosting | |
| First to Six | |
| Scrambled Tickets | |
| Weighted Keys | |
| The Brackets Problem | |
| P-value to a Layman | |
| Prime to N | |
| Job Recommendation | |
| 500 Cards |
Synthesized from candidate reports. Individual experiences may vary.
One intern candidate reports a first round that combined a simple coding problem, incrementing a large number, with ML questions. Those covered logistic regression, stochastic versus batch gradient descent, and basic classification. Expect to explain your reasoning rather than only recite definitions.
The same intern candidate describes a second round that walked through their resume in detail, focused on ML projects. It then moved to a case study on designing an ML pipeline for a real-world problem, including preprocessing, model selection, and evaluation. Be ready to discuss trade-offs in your own work.
A senior ML candidate reports a data structures round framed around web crawling for an e-commerce site. The interviewer asked them to implement a tree traversal algorithm and walk through it. That candidate found it unexpected for an ML role and did not receive an offer. Other candidates did not mention this round.
One intern candidate reports a team ML round on generative AI and recent papers with sharp follow-ups, then a manager conversation about past projects and fit with no coding. The senior candidate also reports a hiring manager interview. Details of that interview were not given, so expect a discussion of your background and fit.
