LinkedIn Machine Learning Engineer Interview: Questions, Expert Tips & 2026 Breakdown

LinkedIn is the world’s largest professional network, focused on helping professionals achieve more in their careers. Our mission is to create economic opportunity for every member of the global workforce and foster an environment where everyone feels a true sense of belonging.

As a Machine Learning Engineer at LinkedIn, you will work on groundbreaking AI technologies that redefine user interactions across LinkedIn’s diverse platforms. Expect technical interviews that cover coding, machine learning algorithms, and design problems. The process will challenge your understanding of the latest ML techniques, reinforcing your expertise in this rapidly evolving field.

Prepare to join a team that values skill and collaboration and dive into a role pushing the boundaries of AI innovation. Explore our Interview Query guide for detailed insights and tips on acing your interview, especially highlighting the LinkedIn machine learning engineer interview questions and how to tackle them.

The interview process usually depends on the role and seniority; however, you can expect the following on a LinkedIn machine learning engineer interview:

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted few, a recruiter from the LinkedIn Talent Acquisition Team will contact you and verify key details like your experiences and skill level. Behavioral questions may also be part of the screening process.

The LinkedIn hiring manager sometimes stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will invite you to the technical screening round. Technical screening for the LinkedIn Machine Learning Engineer role is usually conducted through virtual means, including video conference and screen sharing. Questions in this one-hour interview stage may revolve around coding questions, algorithms, data structures, and machine learning concepts.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds will be conducted during your day at the LinkedIn office, varying with the role. These rounds may include:

1. Coding Interviews: Initially conducted virtually or onsite to assess your problem-solving skills. Expect questions that could be medium to hard on platforms like LeetCode (e.g., tree and graph problems, coding a function to sample from a non-uniform distribution, etc.).

2. Machine Learning System Design: You might be asked to design systems like recommendation engines or discuss machine learning concepts in depth. Proficiency in cross-validation, logistic regression, and deep learning approaches will be assessed. Probability theory questions are common and can be intensive.

3. Behavioral Interviews: Hiring managers or team leads may ask about past projects, team experiences, and scenarios to gauge your fit within the company’s culture.

4. Technical and Practical ML Questions: You should be prepared for open-ended discussions and practical problem-solving, often revolving around real-world applications and the theoretical underpinnings of machine learning models.

Remember, LinkedIn’s interview process often includes a lunch round, which, while informal, also serves as an opportunity to network and better understand LinkedIn’s culture.

Typically, interviews at LinkedIn vary by role and team, but commonly, Machine Learning Engineer interviews follow a fairly standardized process across these question topics.

1. Create a function combinational_dice_rolls to dump all possible combinations of dice rolls.

Given n dice each with m faces, write a function combinational_dice_rolls to dump all possible combinations of dice rolls.

Bonus: Can you do it recursively?

2. Write a query to retrieve the number of users that have posted each job only once and the number of users that have posted at least one job multiple times.

Given a table of job postings, write a query to retrieve the number of users that have posted each job only once and the number of users that have posted at least one job multiple times. Each user has at least one job posting.

3. Create a function to select a random number from a stream with equal probability and O(1) space.

Given a stream of numbers, create a function to select a random number from the stream with equal probability and O(1) space in selection.

4. Write a function pick_host to determine the optimal friend to host a party based on location.

Given a group of friends represented by a list of dictionaries with their names and locations on a three-dimensional scale, write a function pick_host to return the friend that should host the party.

5. Develop a function sort_lists to merge sorted integer lists while maintaining sorted order.

Given a list of sorted integer lists, write a function sort_lists to create a combined list while maintaining sorted order without importing any libraries or using the ‘sort’ or ‘sorted’ functions in Python.

6. Why has the number of job applicants been decreasing despite stable job postings?

You are looking at job board metrics where job postings per day have remained stable, but the number of applicants has decreased. Why might this be happening?

7. What are type I and type II errors in hypothesis testing?

In hypothesis testing, what are type I errors (false positives) and type II errors (false negatives)? What is the difference between them?

Bonus: Describe the probability of making each type of error mathematically.

8. How would you analyze the performance of a new LinkedIn feature without an A/B test?

You are a data scientist at LinkedIn, and a new feature allows candidates to message hiring managers directly. Due to engineering constraints, you can’t A/B test the feature before launching it. How would you analyze its performance?

9. How would you differentiate between scrapers and real people in a dataset of page views?

Given a dataset of page views where each row represents one page view, how would you differentiate between scrapers and real people?

10. How would you design an A/B test to evaluate a pricing increase for a B2B SAAS company?

You work at a B2B SAAS company and are interested in testing different subscription pricing levels. Your project manager asks you to run a two-week-long A/B test to test an increase in pricing. How would you design this test, and how would you determine if the pricing increase is a good business decision?

11. What’s the probability of drawing three cards in increasing order from a shuffled deck of 500 cards?

Imagine a deck of 500 cards numbered from 1 to 500. If all the cards are shuffled randomly and you are asked to pick three cards, one at a time, what’s the probability of each subsequent card being larger than the previously drawn card?

12. What is the probability that it’s actually raining in Seattle, given your friends’ responses?

You call 3 random friends in Seattle to ask if it’s raining. Each has a ²⁄₃ chance of telling the truth and a ¹⁄₃ chance of lying. All 3 say “Yes.” Calculate the probability that it is actually raining.

13. Can you create an algorithm to generate uniformly distributed zeros and ones using an unfair coin?

Given an unfair coin with unequal probabilities for heads and tails, can you devise an algorithm to generate a list of uniformly distributed zeros and ones using only the results of the coin tosses?

14. What is an unbiased estimator, and can you provide a simple example?

Explain what an unbiased estimator is and provide an example that a layman can understand.

15. What’s the difference between Lasso and Ridge Regression?

Explain the key differences between Lasso and Ridge Regression, focusing on their regularization techniques and how they handle feature selection and coefficients.

16. How would you explain loan rejection without access to feature weights?

You have a binary classification model for loan approval but lack access to feature weights. Describe how you would generate reasons for each rejected applicant.

17. How would you determine if a new delivery time estimate model is better than the old one?

You want to build a new delivery time estimate model for food delivery. Explain how you would compare the new model’s predictions to the old model’s to determine which is better.

18. What are the benefits of dynamic pricing, and how can you estimate supply and demand?

Discuss the advantages of dynamic pricing and describe methods to estimate supply and demand in this context.

19. How would you build a job recommendation feed using LinkedIn profiles and job application data?

You can access LinkedIn profiles, job application data, and user responses to job search questions. Explain how you would use this information to build a job recommendation engine.

Preparing for a LinkedIn machine learning engineer interview requires balancing strong machine learning fundamentals with an understanding of how models operate at massive, real-world scale. LinkedIn’s engineering teams build systems that power search, recommendations, feed ranking, and trust across more than one billion members. Interviewers look for engineers who can translate theory into reliable production systems while collaborating closely with product, data, and infrastructure partners.

Strengthen end-to-end machine learning fundamentals

LinkedIn machine learning engineer interviews emphasize full-lifecycle thinking. You should be comfortable discussing how models move from data collection to training, evaluation, deployment, and monitoring. Review supervised and unsupervised learning techniques, feature engineering strategies, and model evaluation metrics, especially precision, recall, and ranking-based metrics used in recommender systems.

Expect to explain trade-offs between model complexity, latency, and maintainability. Interviewers value engineers who can justify simpler approaches when they are more robust in production.

Tip: Practice explaining your modeling decisions as if you were handing the system off to another engineer.

Practice applied coding and data structures

Coding interviews at LinkedIn focus on clean, readable solutions rather than puzzle-heavy algorithms. Review core data structures, algorithmic problem-solving, and writing efficient code in a shared environment. You may also be asked to manipulate data or implement components related to machine learning pipelines.

Strong candidates narrate their thought process clearly, handle edge cases, and test their code methodically.

Tip: Practice solving problems aloud while focusing on correctness and clarity, not speed alone.

Prepare for system design and scalability discussions

System design interviews are a critical part of the LinkedIn machine learning engineer loop, especially for mid-level and senior roles. Be ready to design scalable machine learning systems such as recommendation pipelines, real-time ranking services, or offline training workflows. You should understand data ingestion, feature stores, model serving, monitoring, and failure handling.

Interviewers want to see how you reason about scale, data freshness, and system reliability across LinkedIn’s global infrastructure.

Tip: Anchor designs in realistic constraints like latency budgets, traffic spikes, and data consistency.

Review behavioral stories with collaboration in mind

LinkedIn places strong emphasis on collaboration, empathy, and inclusive decision-making. Behavioral interviews explore how you work with cross-functional partners, handle disagreements, and make data-driven decisions under ambiguity. Prepare examples that demonstrate ownership, technical leadership, and the ability to influence without authority.

Clear communication and reflection matter as much as outcomes.

Tip: Use concise stories that highlight both technical depth and teamwork.

Study LinkedIn’s products and machine learning use cases

Understanding how LinkedIn applies machine learning strengthens your answers across all interview stages. Review how models support member recommendations, job matching, feed relevance, and trust and safety. Connect your past experience to these use cases when discussing projects or design choices.

Showing product awareness signals that you can align engineering decisions with member value.

Tip: Tie your technical choices back to user impact and business goals.

In the end, succeeding in LinkedIn’s machine learning engineer interview means demonstrating strong fundamentals, scalable system thinking, and collaborative decision-making at every stage. Interview Query’s mock interviews let you rehearse role- and company-specific rounds, from coding and ML design to behavioral, so you can walk into each interview confident and prepared.

What is the average salary for a Machine Learning Engineer at LinkedIn?

What is the culture like at LinkedIn?

LinkedIn fosters an environment where every team member feels a sense of belonging and is encouraged to take risks and innovate. The company values diverse viewpoints and promotes a collaborative atmosphere where everyone supports each other’s career growth.

Are there any tips for improving performance during the interviews?

Interviewers value clear and efficient communication. Avoid spending too much time on non-essential details. Use the provided hints, ask clarifying questions, and aim to demonstrate your problem-solving approach comprehensively and practically.

The LinkedIn interview process for a Machine Learning Engineer position is challenging and enlightening. Being well-prepared for questions ranging from data structures and algorithms to in-depth ML system design problems is essential.

Explore our dedicated LinkedIn Interview Guide on Interview Query to boost your preparation. Here, you’ll find a wealth of resources, including common interview questions, detailed role insights, and strategies to tackle each interview stage confidently.

Good luck with your interview journey!