LinkedIn Machine Learning Engineer Interview Questions

LinkedIn·Machine Learning Engineer·Technical Phone Screen

Jul 2026

LinkedIn MLE interview with a probability/geometry question that sounds straightforward but has a real gotcha buried in it. Not a coding marathon, more of a math reasoning session.

How would you sample a point (x, y) uniformly at random from the area of a circle with radius R centered at the origin? Why does picking the radius uniformly from [0, R] fail, and what's the correct approach?

“I knew something was off with the naive approach but couldn't articulate it cleanly at first.”

View Post

LinkedIn·Machine Learning Engineer·Technical Phone Screen

Jul 2026

LinkedIn ML engineer interview with a geometry/math-flavored coding problem. Pretty short content to go on but the question itself is deceptively tricky if you haven't seen triangle inequality before.

Given an array of integers, find three elements that can form the sides of a valid triangle.

“I knew the triangle inequality theorem but blanked on how to apply it efficiently.”

View Post

LinkedIn·Machine Learning Engineer·Technical Phone Screen

Jun 2026

LinkedIn ML Engineer interview with a couple of decision tree questions. Nothing too crazy but the second one had a lot of moving parts and I felt like I was rambling by the end.

For a classification tree, does a leaf node have to output exactly 0 or 1? Explain your reasoning.
Compare two trained trees: one where every leaf has exactly one training sample, and another where every leaf has multiple samples. Which overfits more, and why? Bring in variance, model capacity, and how regularization parameters like max depth and minimum samples per leaf relate to this.

“Easier than it sounds but I almost over-explained it.”

View Post

LinkedIn·Machine Learning Engineer·Technical Phone Screen

Jun 2026

LinkedIn ML engineer interview that was pretty focused on probability and sampling fundamentals. One coding question with a follow-up that tripped me up a bit.

Given a probability distribution over M outcomes, implement a sampler that picks an index proportional to its probability. Build the prefix sum array and write the binary search from scratch.
Follow-up: what do you do if the input probabilities don't sum to exactly 1.0?

“Prefix sums clicked fast but I fumbled the binary search a little.”

View Post

LinkedIn·Machine Learning Engineer·Technical Phone Screen

Jun 2026

LinkedIn ML Engineer interview with a coding-heavy question on stratified sampling from a JSON-structured dataset. One meaty problem that covered implementation, edge cases, and a theory discussion about sampling bias on imbalanced data.

Given a JSON object where each key is a class label mapping to a list of samples, implement a stratified sampling function that either samples uniformly within each class or draws N total samples with proportional class representation. The solution must be seeded for reproducibility and handle edge cases like empty classes, oversized sample requests, and single-class inputs.
Why is stratified sampling preferable to flat uniform sampling for imbalanced datasets, and what bias does flat sampling introduce?

“This took longer than I expected to get right.”

View Post

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.”

View Post

LinkedIn·Machine Learning Engineer·Technical Phone Screen

Jun 2026

LinkedIn ML engineer round that was basically a disguised data structures problem wrapped in a product-y shell. Three parts to one big coding question, and the last part is where things got interesting.

Design and implement an alert-stream processor that can report all alerts received in the last 15 minutes.
For the same alert-stream system, implement a function that returns the per-second severity distribution within the current rolling hour.
Add spike detection to the alert processor: for each minute bucket, find the index of the next minute that has a strictly higher alert count, or return -1 if no such minute exists.

“Pretty much a sliding window question.”

View Post

LinkedIn·Machine Learning Engineer·Technical Phone Screen

Jun 2026

LinkedIn ML engineer interview, one question about building a location-aware job recommendation feature. Pretty thin on details but the core problem was interesting enough to think through.

How would you design a system to show job postings to users in the same city, personalized based on their past search behavior?

“I went straight to the retrieval side, filtering by geo and then ranking with a learned model on search history.”

View Post

LinkedIn·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026

LinkedIn system design round for an ML Engineer role, which ended up being a deep distributed systems question that felt more backend-heavy than I expected for the position. The breadth of what they wanted covered was a lot to fit into one session.

Design a distributed key-value storage service that supports high availability across zones, horizontal scalability to billions of keys, low-latency reads and writes, per-key read-after-write consistency, TTL, conditional updates, and optional range scans. Walk through the full design including API, sharding, replication, read/write paths, on-disk storage, failure handling, and observability.

“This is basically a 'design DynamoDB but explain every decision' question and I was not ready for the full scope of it.”

View Post

LinkedIn·Machine Learning Engineer·Technical Phone Screen

Jun 2026

LinkedIn ML Engineer screen, pretty heavy on ML fundamentals. One question but they really wanted you to go deep on it, not just recite a definition.

Why is logistic regression a good fit for binary classification, and how can you justify treating its output as a probability?

“This sounds like a warmup question until they ask you to actually derive things.”

View Post