LinkedIn AI Interview Questions — Acing the AI Interview

AI is like oxygen at LinkedIn, it permeates every single member experience — Deepak Agarwal, Head of AI, LinkedIn

Every professional in the world knows about LinkedIn and has a profile on it. Also, its data prowess comes from the fact that it is a professional network, the quality of data it has is better and more accurate than other networks. Google, Apple, Amazon and Microsoft are all older than LinkedIn but the fact that LinkedIn has amassed so much data for AI in just 15 years is very commendable.


At Acing AI, the aim is to help you to get into Data Science and AI. I have profiled some of the best technology companies and written articles about AI interviews at Microsoft, Google, Amazon, LinkedIn, Ebay, Twitter, Walmart, Apple, Facebook, Zillow, Salesforce, Uber, Intel, Adobe Tesla and most recently IBM. This has led to being the top writer in Artificial Intelligence on Medium. The AI interview preparation guides Part 1, Part 2 go over the details which help you ace any AI interview. Acing AI Portfolios helps you to showcase your AI work. Expert interviews and analyses gives you a sneak peak into the lives of AI/Data Science Leaders and analyses of AI tech companies. Now onto the LinkedIn Data Science Questions article…

Source: Link

Even though Microsoft acquired LinkedIn for 26B$ LinkedIn, per its CEO LinkedIn still has freedom to grow the company in their own way. But for fiscal year 2017, the service brought in just $2.3 billion, or 2.5% of Microsoft’s total annual revenue. Microsoft’s new Bing Ad service leverages LinkedIn, Graph API and AI foundations. The LinkedIn AI team sits within its LinkedIn data team within the company.

Interview Process

LinkedIn has a typical interview process like most other companies who hire Engineers. The Data Science roles usually have a process tweaked a little which reflects the importance of different aspects under the umbrella of Data Science. There are usually phone interviews(involve coding) followed by onsite interviews. Onsite there are about 4–5 interviews. There might be 2–3 of them really going deep on Data Science related questions, research and models. The remaining ones are aimed to test the coding skills.

Important Reading
Source: Photon ML
  1. LinkedIn’s scalable ML library for Spark: Photon ML
  2. ACM Deep Learning for personalized search/recommender systems: Video
  3. Talent Blog Article: How LinkedIn uses AI to power recruiting tools
  4. LinkedIn Engineering/AI Blog: AI and Relevance
AI/Data Science Related Questions
  • Given a random generator that produces a number 1 to 5 uniformly, write a function that produces a number from 1 to 7 uniformly.
  • Segment a long string into a set of valid words using a dictionary. Return false if the string cannot be segmented. What is the complexity of your solution?
  • How many lines do you think a LinkedIn users’ daily login table has?
  • How many active members in LinkedIn right now? What is the business model at LinkedIn?
  • How many cubes are exposed in a Rubik’s cube?
  • There is a significant increase in LinkedIn signups in this month. How much of this will you attribute to the changes you made in signup process. What data-points would you look into to confirm/deny this?
  • Which part of our product you dislike most? Then can you think of the reasons why we decided to make it that way? And how would you quantify its badness (goodness)? How would you fix it? And why it will fix it?
  • Find the second largest element in a Binary Search Tree
  • Find out k most frequent numbers from incoming stream of numbers one the fly
  • How would you design the PYMK — “people you may know” application of LinkedIn?
  • Tell us a LinkedIn product problem. Solve the product problem by designing a multivariate A/B test.
  • What product metrics do you construct? How to tell if your experiment is successful?
  • What is the optimization problem for a SVM?
  • Design and implement Java iterator for nested containers.
  • Implement the pow function.
  • Describe 3 kernel functions and when to use which of them.
  • Generate a sorted vector from two sorted vectors.
  • Design a recommendation engine for jobs.
  • Describe collaborative filtering.
  • Describe the different classification and prediction models. (k-means clustering, bayesian model, decision trees )
Reflecting on the Questions

LinkedIn interviews many coding and product related questions. Product questions require deep product based thinking on the fly. This is different from the other companies we have looked at previously. The questions are also focused on different data applications and recommendation systems which are deployed in their product.

Consumable List: 20 LinkedIn AI Interview Questions

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