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LinkedIn interview notes, page 26 of 32

Large Enterprises

Most of these LinkedIn write-ups come from Software Engineers (156), then Product Managers (66) and Data Scientists (37). The most reported rounds are the technical phone screen (169), the product sense round (62), the system design round (42) and the behavioral / leadership round (20).

316 interview notes · updated Oct 2026

LinkedIn·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
LinkedIn product sense question about whether they should spin off a standalone messaging app. Not a lot of context given, just thrown into the deep end.
  • Should LinkedIn launch a separate standalone messaging app?

“I started by questioning whether LinkedIn's in-app messaging is actually a pain point worth solving with a whole new product.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026
LinkedIn software engineer interview with a coding round focused on data structure design. The problem looked manageable at first but the edge cases during deletion are where things get tricky and where I suspect most people trip up.
  • Design a data structure that stores integers with duplicates and supports insert, remove, and getRandom, all in expected O(1) time. Insert should return whether the value is new to the collection, remove should return whether anything was actually deleted, and getRandom should pick uniformly across all stored occurrences including duplicates.

“The insert and getRandom parts clicked pretty fast.”

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LinkedIn·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
LinkedIn product interview, one question about pitching a new analytics platform to stakeholders. Not much else to go on but it was a meaty enough prompt to chew on for a while.
  • How would you convince stakeholders to launch a new analytics platform for LinkedIn?

“I went straight into business value and ROI framing, which felt right at the time but I think I skipped too fast past the part where you actually figure out who the stakeholders are and what they care about individually.”

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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·Software Engineer·Technical Phone Screen

Jun 2026
LinkedIn coding round, one question about implementing a cache with a custom eviction policy. Pretty focused session, no fluff.
  • You're given a cache interface where values implement a Rankable interface with a rank() method. Implement the cache with put and get operations such that when capacity is full, the entry with the lowest rank() gets evicted.

“The hashmap part was obvious but I fumbled for a bit on the eviction side.”

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LinkedIn·Product Manager·Hiring Manager Screen

Jun 2026
Screened for a PM role at LinkedIn and got asked about AI prototyping tools. Pretty short conversation, felt more like a vibe check than a deep technical dive.
  • Are you familiar with AI prototyping? What tools or platforms have you used, and how has that experience been for you?

“I rattled off a few tools I'd used and tried to tie it back to actual product decisions I'd made with them.”

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LinkedIn·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
LinkedIn PM interview with a product design question focused on building a travel feature for Instagram. Pretty open-ended, which I wasn't fully prepared for.
  • How would you design a travel product for Instagram?

“I went straight into features without anchoring on the user first, which I regretted about two minutes in.”

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LinkedIn·Software Engineer·Onsite - Multi Round

Jun 2026
Went through five rounds for a software engineering role at LinkedIn, all virtual. Most rounds felt solid but the AI-assisted coding round was a mess due to technical issues on their end, and that's the one that came back with negative feedback.
  • Solve two algorithmic coding problems under timed conditions.
  • Complete a coding problem in a round that uses an AI-assisted coding environment.
  • Design a system to meet a given set of requirements.
  • Walk through a past project and discuss technical decisions you made.
  • Discuss your background and experience with the hiring manager.

“The phone screen had two problems back to back.”

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LinkedIn·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Product strategy question for LinkedIn, just the one question about whether they should build a video conferencing tool. Pretty open-ended, no clear right answer, which made it both interesting and stressful.
  • Should LinkedIn build and launch its own video conferencing platform?

“I went straight to competitive landscape and probably spent too long on Zoom and Teams before actually thinking about what LinkedIn users would even want from a video product.”

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LinkedIn·Machine Learning Engineer·Technical Phone Screen

Jun 2026
LinkedIn ML engineer interview covering a solid mix of practical foundations: calibration, feature selection, tokenizers, and optimizers. Nothing too exotic but the questions had enough depth that you couldn't just wave your hands through them.
  • Your model outputs probabilities. When does that actually matter for calibration, and how would you go about calibrating it? How do you even measure whether calibration is good?
  • Walk me through why you'd do feature selection at all, what methods you'd use across filter, wrapper, and embedded approaches, and how you'd use a neural network to do it.
  • What is a tokenizer, what are the main types like BPE, WordPiece, and unigram, and what practical trade-offs should you care about when choosing one?
  • Explain Adam at a high level, what internal state it tracks and why, and describe situations where it fails or needs careful tuning.

“This one tripped me up more than I expected.”

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