LinkedIn AI Engineer Interview Questions

LinkedIn·AI Engineer·Technical Phone Screen

Jun 2026

LinkedIn AI Engineer interview with a machine learning system design question centered on collaborative filtering. The problem was more implementation-heavy than I expected, pushing into data structure territory pretty quickly.

Given a sparse user-movie rating matrix, how would you use kNN to predict a target user's rating for a movie they haven't seen? Walk through your similarity metric choice, neighbor selection, and the data structures you'd use to make it efficient.

“I started with cosine similarity because it felt safer than Pearson and I could explain it faster under pressure.”

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LinkedIn·AI Engineer·Onsite - System Design / Architecture

Jun 2026

LinkedIn AI Engineer system design round, one big question that basically ate the whole session. The scope was genuinely massive and I'm still not sure I covered even half of it well.

Design an AI-powered personalized InMail system for recruiters, covering the full end-to-end flow: recruiter UI for candidate selection and message preview, candidate and job context retrieval, prompt construction and LLM serving, personalization signals, sending and tracking pipeline, feedback loop for model improvement, and considerations around latency, cost, abuse prevention, and PII.

“This was a single question that ran the entire interview.”

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LinkedIn·AI Engineer·Onsite - System Design / Architecture

Jun 2026

LinkedIn AI Engineer system design round, one big open-ended question about building a job categorization system that can also invent new categories on the fly. Dense problem with a lot of moving parts and I'm not sure I covered everything they wanted.

Design an ML system that takes a new job posting and either maps it to an existing job category or proposes a brand-new category when nothing fits. Walk through text representation, classification approach, how you'd decide when to trigger a new category, how new categories get created and named, your labeling pipeline, inference strategy, evaluation, and how the taxonomy evolves over time.

“This is a sprawling question and I underestimated how much ground they actually wanted covered.”

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LinkedIn·AI Engineer·Onsite - Behavioral / Leadership

Jun 2026

Behavioral round at LinkedIn for an AI Engineer role. Just one question but it had real teeth to it, the kind where a vague answer is obvious immediately.

Tell me about a time you were blocked by a dependency outside your control, like another team's deliverable or missing infrastructure. How did you diagnose what was actually blocking you, what did you do to move things forward, and what happened?

“The diagnosis part is what tripped me up.”

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LinkedIn·AI Engineer·Onsite - Behavioral / Leadership

Jun 2026

Behavioral round for an AI Engineer role at LinkedIn. The whole thing was project-deep-dives, but with a specific twist: they wanted to hear about impact well beyond just shipping the thing, think org-level ripple effects, not just latency numbers.

Walk me through a project you worked on. What was the business or user problem, what did you specifically do, and what was the measurable impact?
Beyond your direct contribution, what broader organizational influence came out of this project? Did other teams reuse your work, or did it affect hiring or mentorship in any way?
How did you connect the technical decisions you made on this project to outcomes the company actually cared about, like revenue, reliability, or productivity?

“This sounds like a standard behavioral prompt until you realize they're not satisfied with 'we reduced latency by 30%.' They kept pushing on what happened after.”

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LinkedIn·AI Engineer·Onsite - System Design / Architecture

Jun 2026

LinkedIn AI Engineer system design round focused entirely on building a recruiter outreach message generation tool end to end. Dense question covering basically every layer of the stack, from data retrieval to LLM serving to compliance. Left feeling like I covered maybe 70% of what they wanted.

Design a system that helps recruiters write personalized outreach messages to candidates using an LLM. Walk through the full pipeline from data ingestion to message generation.
What data sources would you use to personalize the outreach message, and how would you store and retrieve them?
How would you construct the prompt sent to the LLM, and what tradeoffs are involved?
How would you handle latency and cost for LLM serving at scale, and what's your fallback strategy?
How would you evaluate whether the system is working well, both offline and in production?
How would you handle PII and potential bias in the generated messages?
How would you use recruiter edits to improve the system over time?

“This is a beast of a question.”

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LinkedIn·AI Engineer·Technical Phone Screen

May 2026

LinkedIn AI Engineer technical screen, one meaty probability/sampling question that took up most of the time. Pretty focused session, no fluff.

You have an N-sided die where each face has a probability derived from a softmax over a logits vector. Implement an efficient sampler that returns a face index according to those probabilities. Walk through your approach and any alternatives you know.

“I went straight for cumulative sum plus binary search, which is the standard inverse-CDF move.”

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LinkedIn·AI Engineer·Technical Phone Screen

May 2026

LinkedIn AI Engineer interview with a math-heavy coding question that felt more like a stats exam than a typical algo round. One question, but it had layers.

Given a 1D array of coordinates, find the point c that minimizes the total sum of absolute distances to all points. Prove mathematically that the optimal c is the median, then implement it efficiently.

“I knew the answer was the median from some vague memory but actually proving it on the spot was a different story.”

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LinkedIn·AI Engineer·Technical Phone Screen

May 2026

Had a technical phone screen for an AI Engineer role at LinkedIn. One algorithm question, pretty focused, they wanted both the solution and a complexity proof which I wasn't fully expecting.

Given the root of a binary search tree and two nodes p and q, find their lowest common ancestor. Also prove the time complexity of your solution.

“The traversal logic itself clicked pretty fast.”

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LinkedIn·AI Engineer·Technical Phone Screen

May 2026

LinkedIn AI Engineer interview with a probability and randomness problem that sounds approachable until you're actually in it. The core challenge was building a fair uniform distribution over 7 values using only a biased coin you know nothing about.

You have a function that returns 0 with unknown probability p and 1 with probability 1-p. Using only this function, write code that returns each integer from 0 to 6 with equal probability.

“I knew the Von Neumann trick from somewhere in the back of my brain, pairs of calls where 01 maps to 0 and 10 maps to 1 and you just retry on 00 or 11.”

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