LinkedIn Interview Questions — Page 23

LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026

LinkedIn SWE interview with a twist: they gave you a CoderPad environment with an AI agent baked in and actually wanted you to use it, which I was not expecting at all. The coding problem itself was calendar scheduling, pretty classic, but the whole 'show us how you work with AI' angle made it feel different from any other technical screen I've done.

Given a list of meetings each with a start and end time, and a duration d for a new meeting, find the earliest possible start time where the new meeting fits without overlapping any existing meeting. Optionally, also accept a working-hours window or an earliest-allowed start as a constraint.
What is the time complexity of your solution?
What test cases would you write for this problem?

“I'd done scheduling problems before so the core logic wasn't the scary part.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026

LinkedIn SWE interview with a two-part coding question that mixed a classic grid problem with something way more niche. The second part threw me off more than I expected.

Given a binary grid where 1 is land and 0 is water, count the number of distinct islands (connected regions of land cells, connected horizontally or vertically).
Given two N-ary trees where each node has a key and some payload, merge them: nodes with matching keys get their payloads merged (values from tree B overwrite tree A), children are matched by key recursively, and any node that only exists in one tree is kept as-is in the result.

“Pretty standard flood-fill problem.”

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LinkedIn·Machine Learning Engineer·Technical Phone Screen

Jun 2026

LinkedIn ML engineer interview, one question about building a location-aware job recommendation feature. Pretty thin on details but the core problem was interesting enough to think through.

How would you design a system to show job postings to users in the same city, personalized based on their past search behavior?

“I went straight to the retrieval side, filtering by geo and then ranking with a learned model on search history.”

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LinkedIn·Software Engineer·Onsite - System Design / Architecture

Jun 2026

LinkedIn system design round focused entirely on building a metrics platform from scratch, think Datadog or Prometheus but you have to justify every layer yourself. Solid challenge, lots of ground to cover.

Design a metrics collection and query platform similar to Datadog or Prometheus, covering agent-based collection from many hosts, an ingestion pipeline, time-series storage with compression and downsampling, a query layer for dashboards and ad-hoc queries, multi-tenant isolation, and independent scaling of ingest versus query. Alerting is out of scope.

“This is basically a full distributed systems design in one question.”

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LinkedIn·Data Scientist·Technical Phone Screen

Jun 2026

Technical screen for a Data Scientist role at LinkedIn covering ML fundamentals. Four meaty questions back to back, all conceptual but with enough depth that you couldn't just recite definitions and move on.

You're building a binary classifier on a highly imbalanced dataset. How do you handle the imbalance during training, and how do you evaluate the model?
Your full dataset is too large to train on directly, so you use a sample. How do you verify the sample is representative, and that the model will generalize to the full population?
You're using a tree-based model and it's overfitting. What do you do?
Why do L1 and L2 regularization introduce bias, and why would you use a biased estimator if an unbiased one exists?

“This one is broader than it looks.”

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LinkedIn·Software Engineer·Recruiter / HR Screen

Jun 2026

Went through a screen at LinkedIn that was pretty much just the classic strengths and weaknesses question. Not much else to report.

What are your two greatest strengths and your biggest weakness?

“Pretty standard opener.”

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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·Software Engineer·Onsite - System Design / Architecture

Jun 2026

LinkedIn system design round for a software engineering role. The whole thing was one big open-ended question about building a company-wide monitoring platform, and they expected you to go pretty deep across a lot of dimensions. Felt like a gauntlet.

Design a company-wide monitoring system that covers metrics, logs, traces, and events from both services and infrastructure. Walk through ingestion at scale, time-series storage, querying and dashboarding, alerting with on-call routing, multi-tenancy across teams, data retention with downsampling, high availability of the monitoring stack itself, and the trade-offs between push and pull collection models.

“This is basically five interviews in one question.”

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LinkedIn·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026

Behavioral round for a staff-level data engineering role on LinkedIn's security side. Two questions, both pretty standard but with enough nuance that you can't just wing them. The focus on infosec context made it feel slightly different from a typical data eng screen.

Walk me through a project you worked on end-to-end. What was the problem, what did you build, and what were the concrete impacts across dimensions like security, reliability, cost, or developer productivity?
Describe a time you had to step away from a project where you were the only person who really understood it. What did you do to make sure things didn't fall apart, and what happened?

“This sounds easy until you realize they want numbers across multiple dimensions, not just 'we improved latency.' I went with a detection pipeline I'd rebuilt and had decent metrics on throughput and alert fidelity, but fumbled when they pushed on cost impact.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026

LinkedIn SWE interview with a modified meeting scheduler design problem. Pretty focused on OOD and knowing your data structures cold, less hand-holdy than I expected.

Design a booking system with two functions: one to record a booking given a start and end time, and another to find the earliest available slot at or after a given start time that fits a requested duration without overlapping existing bookings. Define your data structures, handle edge cases, and walk through the complexity.

“I went straight to a sorted list of intervals and built from there.”

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