Adobe Machine Learning Engineer Interview Questions

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·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·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·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Adobe ML engineer interview that went pretty deep into inference optimization, specifically the KV cache. Not the most brutal round I've had but you need to actually understand the mechanics, not just know the term.

How does the KV cache work in transformer inference, and why do we cache keys and values but not queries?
Why does KV caching matter specifically for decoder-only models, and why does it not provide the same benefit for encoder architectures?
What are the memory tradeoffs involved in KV caching as sequence length grows?

“This is where I started strong and then slightly fumbled the Q part.”

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Adobe·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Adobe ML engineer interview that leaned hard into transformer architecture fundamentals. The questions were focused and technical, no fluff, just a deep dive into how attention mechanisms actually work under the hood.

What is the difference between encoder and decoder transformers, and what are they each used for?
How does bidirectional attention differ from causal masked attention, and why does each approach exist?
How does cross-attention work in an encoder-decoder architecture?
Why don't encoder-only models need a KV cache during inference?

“Felt okay about this one.”

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Adobe·Machine Learning Engineer·Technical Phone Screen

May 2026

Adobe ML engineer interview with a graph/BFS problem. Pretty standard coding round, nothing too wild, but the problem has a few gotchas if you're not careful about how you initialize the BFS.

Given a binary matrix of M rows and N columns, compute the shortest distance from each cell to the nearest cell containing 0. Return the result as a new matrix of the same dimensions.

“Multi-source BFS is the right move here.”

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Adobe·Machine Learning Engineer·Onsite - Multi Round

May 2026

Went through a pretty thorough loop at Adobe for an ML Engineer role. It was a mix of behavioral and technical stuff, resume deep-dive included, and they clearly wanted to see how you think about ambiguity and cross-team work, not just model accuracy.

Walk me through your resume from start to finish, and highlight two projects you're especially proud of. What were your specific contributions, the technical decisions you made, and what measurable outcomes came out of them?
Tell me about a time you had to work through a situation where the requirements or direction were unclear. How did you move forward?
Describe a conflict you had with a teammate or stakeholder. How did it play out and what did you do to resolve it?
Can you give an example of a time you had to deliver something under a really tight deadline? What trade-offs did you make?
Tell me about a failure. What happened and what did you take away from it?
Why Adobe specifically, and how do you typically work with marketing teams or other non-technical partners?

“This took way longer than I expected.”

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Adobe·Machine Learning Engineer·Technical Phone Screen

May 2026

Went through a technical phone screen for an ML Engineer role at Adobe that was pretty much entirely focused on numerical precision and mixed precision training. Not a lot of breadth, just one topic drilled pretty deep.

Can you walk through the differences between FP16, BF16, and FP32, and explain why mixed precision training speeds things up?
Why is BF16 generally preferred over FP16 for training large language models?
What are the tradeoffs between a wider exponent range and more mantissa bits in floating point formats?

“This is where I spent most of the interview.”

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Adobe·Machine Learning Engineer·Technical Phone Screen

May 2026

Adobe MLE interview focused heavily on the performance side of transformer systems, the kind of stuff that's easy to handwave but hard to actually explain under pressure. A lot of ground covered around GPU utilization and memory bottlenecks.

Why are transformer models considered memory-bound rather than compute-bound, and what does that mean for performance optimization?
What is the cost of materializing the full attention matrix, and how does it affect memory usage at scale?
Can you explain HFU and MFU and how you'd use them to evaluate GPU efficiency?
How does kernel fusion help with GPU performance, and what role do CUDA graphs play in reducing overhead?

“This is the kind of question where you think you know the answer and then halfway through realize you're describing it wrong.”

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Adobe·Machine Learning Engineer·Onsite - System Design / Architecture

May 2026

Adobe ML Engineer onsite focused entirely on one massive system design question about building an AI assistant for their Experience Platform. No coding, no behavioral, just two hours of whiteboarding an end-to-end RAG architecture with some very pointed follow-ups.

Design a natural-language assistant for Adobe Experience Platform that can both answer questions about product documentation and answer questions about a customer's own tenant data (like dataset counts or audience sizes). Walk through the full architecture, security model, guardrails, latency targets, privacy handling, and evaluation strategy.

“This is a monster of a question and I did not scope it fast enough.”

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