Two candidates describe Samsung ML Engineer interviews that mix a coding question, ML fundamentals such as bias and variance, and a deep dive into past projects. Both report receiving offers.
$203K
Avg. Base Comp
$248K
Avg. Total Comp
3 rounds
Typical Rounds
2-3 weeks
Process Length
Both candidates who describe the Samsung Electronics ML Engineer interview report a mix of algorithm coding, machine learning theory and detailed discussion of past work. Both also report receiving offers.
One candidate describes a recruiter call after a referral, then a 1-hour hiring manager phone screen that opened with a LeetCode-style question. A 40-minute panel with three interviewers followed, centered on presenting past projects. The other candidate describes three rounds: an introductory conversation, a 30-minute medium-level graph problem followed by ML questions, and a final round combining a project deep dive with more advanced algorithm questions.
Bias and variance came up in both accounts, so be ready to explain it clearly. One candidate was asked about regularization and model complexity, and about diagnosing why a model performs poorly on training data. Other reported topics were batch normalization, gradient descent and a detailed explanation of how LSTM works.
Both accounts also include a deep dive into past projects, so prepare to defend your work. Be ready to explain why you chose one model over another, what trade-offs you weighed and what you would change in hindsight. One candidate was also asked about culture fit and commute distance. For coding, practice medium-level graph and data-structure problems.
Synthesized from 2 candidate reports by our editorial team.
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Featured question at Samsung Electronics
Why can the same machine learning algorithm produce different success rates on the same dataset
| Question | |
|---|---|
| Valid Anagram | |
| Hurdles In Data Projects | |
| Bias - Variance Tradeoff and Class Imbalance in Finance | |
| Cyclic Detection | |
| Target Value Search | |
| Bias vs. Variance Tradeoff | |
| Food Delivery Times | |
| Data Preparation for Imbalanced Data | |
| String Palindromes | |
| Deciding Between Solutions | |
| Shortest Path Algorithms | |
| Text Editor With OOP | |
| Seller Type Modeling | |
| Impossibly Iterative Fibonacci | |
| Client Solution Pushback | |
| Your Strengths and Weaknesses | |
| LRU Cache 1 | |
| Regularization and Validation | |
| Gradient Descent Calculation | |
| Bias Variance Tradeoff | |
| Merge Sorted Lists | |
| Find the Missing Number | |
| Maximum Profit | |
| Scrambled Tickets | |
| The Brackets Problem | |
| Prime to N | |
| Bagging vs Boosting | |
| One Element Removed | |
| Random Forest Explanation |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report starting with a recruiter call, in one case after a referral, or a casual introductory conversation about background and projects. One candidate says the recruiter signaled that later rounds would become more technical. Be ready to summarize your projects and ML experience briefly.
One candidate reports a 1-hour hiring manager phone screen that opened with a LeetCode-style question before moving to background and fundamentals. Another describes a roughly 30-minute medium-level graph problem. Practice timed data structure and algorithm questions, since both accounts include them.
Candidates report foundational ML questions in the technical conversations. Reported topics include bias and variance, regularization, why a model might underperform on training data, batch normalization and gradient descent. Prepare to explain each concept and how you would apply it to a real model.
Both candidates describe a late round centered on past projects. One reports a roughly 40-minute panel with three interviewers, covering an LSTM explanation, model selection and culture fit. The other reports a final round that added harder algorithm questions. Prepare a concise project walkthrough and be ready to justify your decisions.
