Candidates describe LinkedIn ML Engineer interviews as a mix of coding, ML fundamentals, resume deep dives, ML system design and behavioral conversations. The reported format varies, so prepare across all of these areas.
$165K
Avg. Base Comp
$230K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
The four candidate reports for LinkedIn ML roles do not describe one fixed loop, so prepare for breadth. Coding plus ML fundamentals appears in several accounts. One candidate describes medium string and graph problems on CoderPad. Another describes an easy-level coding problem alongside bagging and boosting questions, and a ternary search problem that felt tight on time. A third reports a shortlisting round that mixed AI/ML basics with coding.
The ML depth reported is practical. Candidates mention decision trees, AUC, adaptive learning rate methods and ensemble methods such as bagging and boosting. Two accounts also say interviewers probed resume projects and research in detail, so be ready to explain what you built and why. One report also mentions Graph ML and Generative AI as conversation topics.
System design shows up in two reports. One candidate describes two machine learning system-design interviews with shadow interviewers. Another describes designing a recommender system from scratch in a five-round virtual onsite. Practice framing objectives, features, trade-offs and metrics for recommendation problems.
Behavioral conversations were also reported, including a hiring-manager interview and an HR-style final round. Note that two of the four reports come from intern-titled or intern-hosted processes, so treat their details as directional for full-time roles. Communication after interviews was uneven in some accounts, with one candidate waiting weeks and another receiving a scheduled call that turned out to be a rejection. All four reports ended in no offer, so tighten both coding speed and ML explanations before the loop.
Synthesized from 4 candidate reports by our editorial team.
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Real interview reports from people who went through the Linkedin process.
The process included a behavioral interview with the hiring manager and two machine learning system-design interviews. The hiring-manager conversation was a poor experience for me: he seemed distracted and appeared to lose interest very quickly, which made it hard to have a productive behavioral discussion. Both system-design rounds also had shadow interviewers. I was ultimately rejected after the final round. A recruiter scheduled a phone call to discuss the result, which turned out to be a rejection call.
Prep tip from this candidate
Prepare for a hiring-manager behavioral interview and two ML system-design discussions. A recruiter scheduling a post-interview call is not, by itself, an offer signal.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Linkedin
Given two sorted lists, write a function to merge them into one sorted list.
| Question | |
|---|---|
| Job Recommendation | |
| Bagging vs Boosting | |
| 500 Cards | |
| The Brackets Problem | |
| Raining in Seattle | |
| Nearest Common Ancestor | |
| Integer to Roman | |
| Find Duplicate Numbers in a List | |
| Reservoir Sampling Stream | |
| Rejection Reason | |
| Hurdles In Data Projects | |
| Lasso vs Ridge | |
| Biased Random Number Generator | |
| Groups of Anagrams | |
| Merge N Sorted Lists | |
| String Mapping | |
| Target Value Search | |
| Unbiased Estimator | |
| Target Indices | |
| Binary Tree Validation | |
| Real-Time Hashtag Partitioning | |
| Same Characters | |
| Type I and II Errors | |
| Possible Triangles | |
| Max Width | |
| Optimal Host | |
| Shortest Path Algorithms | |
| Combinational Dice Rolls | |
| Optimistic vs Pessimistic Locking |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter conversation after applying online or through a referral. In two accounts it clarified the structure of the process, and one candidate was told to expect three rounds plus a coding assessment. Ask about format and timing, since reports vary.
Candidates report an early technical screen that combines coding with ML basics. Problems described include medium string and graph questions, an easy-level problem, and a ternary search task. Bagging, boosting and general AI/ML fundamentals may come up alongside the code, so practice switching quickly between the two.
Several candidates describe discussions of resume projects and research, plus ML theory such as optimization methods, decision trees and AUC. One report included about 25 minutes of coding in a technical interview with ML engineers. Expect to explain your own work in depth, not just recite definitions.
Two candidates report ML design discussions: two system-design interviews in one account, and a recommender system design in a five-round virtual onsite in another. Practice stating goals, data, features, models and evaluation trade-offs.
Candidates describe a roughly 45-minute hiring-manager session that was mostly behavioral, and a behavioral or HR-focused final round in a separate account. One candidate found the manager distracted. Prepare concise stories about your projects and decisions. In one report, the result took weeks to arrive.
