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Adobe interview notes, page 2 of 6

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·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Adobe ML Engineer interview that came down to a single meaty coding question: implement K-Means from scratch. No libraries, full discussion of tradeoffs and edge cases. Felt more like a whiteboard research conversation than a typical LeetCode grind.
  • Implement K-Means clustering from scratch using only NumPy or plain lists. Your function should accept a set of 2D points, the number of clusters k, a max iteration count, and a convergence tolerance. Walk through centroid initialization, point assignment, centroid recomputation, and stopping conditions. Return both the cluster labels and final centroids. Also discuss time complexity, how you'd handle edge cases like empty clusters or ties, and how you'd pick k in practice.

“This one took up the whole session.”

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Adobe·Product Manager·Hiring Manager Screen

Apr 2026
Adobe PM interview, came down to one question about metrics that felt deceptively straightforward but had some depth to it once I started talking.
  • What are the key metrics you track for the product you currently manage?

“I rattled off a few numbers without really connecting them to outcomes and only realized mid-answer that I was just listing stuff.”

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Adobe·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Adobe system design round focused entirely on one massive question about building a multi-modal embedding pipeline. No warmup, no small talk, just straight into the deep end for the full hour.
  • Design a system that generates and stores embeddings for user-uploaded files across multiple modalities (documents, images, videos), covering the ingestion API, preprocessing per modality, model selection, batching and GPU scheduling, storage strategy, similarity search, deduplication, retry/idempotency handling, model update backfills, monitoring, cost controls, and privacy considerations.

“This is basically five system design questions stapled together.”

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Adobe·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Adobe ML Engineer technical screen focused heavily on transformer internals, specifically Multi-Head Attention. The depth they expected was real, not just surface-level definitions. Came out feeling like I'd either nailed it or completely missed what they were looking for.
  • Walk through the full operation order inside Multi-Head Attention, step by step.
  • Explain the Q, K, V projections and how the tensors get reshaped for multi-head computation.
  • How are attention scores computed and what role does softmax play?
  • Why does Multi-Head Attention use four linear projections total rather than three?
  • Do a FLOPs analysis of the self-attention mechanism.
  • Why is the computational complexity of self-attention O(S^2) with respect to sequence length?

“I started with the Q/K/V projections and worked forward, but I stumbled a bit on where exactly the reshape happens relative to the score computation.”

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Adobe·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Adobe ML engineer round, one coding problem that looked like a straightforward prefix-sum thing but had enough edge cases to make me sweat. The follow-up about streaming caught me completely flat-footed.
  • Given an integer array and an integer k, determine if there's a contiguous subarray of length at least 2 whose sum is divisible by k. If k is zero, find a subarray with sum exactly zero instead. Walk through your approach and give the time and space complexity.
  • How would you adapt your solution if the array arrives as a stream, one element at a time, rather than all at once?

“I knew prefix sums were involved but fumbled the modular arithmetic part for a bit.”

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Adobe·Software Engineer·Take-home Assignment

May 2026
Adobe gave me a React coding task for a full-stack engineer role. It was more involved than I expected for what felt like a standard frontend exercise, mostly because of the role constraint logic and the sorting requirement that the unit tests enforced pretty strictly.
  • Build a React page that displays a player list and a team composition panel. Users can add players to their team, each player has a role with min/max constraints on how many can be added, clicking a player's name opens a detail dialog, and both lists must be sorted alphabetically by player name.

“The role constraint logic is where I lost the most time.”

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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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Adobe·Product Manager·Onsite - Product Sense / Strategy

May 2026
Got a product strategy question for a PM role at Adobe, framed around Chrome Browser of all things. One question, pretty open-ended, and I spent way too long trying to figure out where to even start.
  • You're a growth PM for Chrome Browser. Walk through how you'd build out a 3-year strategy for the browser.

“This one tripped me up more than it should have.”

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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·Data Scientist·Technical Phone Screen

May 2026
Had a round at Adobe for a data scientist role that was clearly testing whether I could translate stats concepts for a non-technical audience. One question, pretty focused, but it required more finesse than I expected.
  • In plain terms, what is a p-value and why does it matter when making product decisions?

“The tricky part isn't knowing what a p-value is, it's explaining it without sounding like a textbook.”

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