Uber Machine Learning Engineer interview questions

65 Machine Learning Engineers have shared notes from their Uber interviews, with the questions they were asked in each round. That is 11% of the 609 Uber interview notes on Screna AI.

65 Machine Learning Engineer interview notes

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

Jul 2026
System design round for an ML Engineer role at Uber. The whole thing was one big design problem about a game-room leaderboard, which sounds simple but ended up going pretty deep into heap internals and complexity tradeoffs.
  • Design a data structure for a sequence of rooms where players complete tasks in order. Support addPlayer, recordTask (with points), moveToNextRoom, getPlayerState, and topK (top K players by total score). Discuss how you'd handle duplicate entries in a heap-based leaderboard, analyze time and space complexity for each operation, and justify your design.

“This took me a while to decompose.”

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Uber·Machine Learning Engineer·Onsite - Coding / Algorithms

Jul 2026
Uber MLE onsite coding round where you pick one of four ML primitives to implement from scratch in Python/NumPy, no sklearn allowed. The math derivation is graded as hard as the code, which I did not fully appreciate going in.
  • Implement linear regression using gradient descent from scratch in NumPy, including the MSE loss and the gradient update rule. Be ready to derive the gradient on a whiteboard before writing any code.
  • Implement logistic regression with mini-batch gradient descent from scratch. Explain the binary cross-entropy loss and justify the gradient. Also handle numerical stability issues like sigmoid overflow and log(0).
  • Build a Markov chain text generator: first a function that builds a frequency map from a corpus (word to next-word counts), then a function that generates text by always picking the most frequent next word.
  • Given N rider coordinates and a target K, find K pickup locations on a grid that minimize the total L1 distance from each rider to its nearest pickup point.
  • Implement multi-head self-attention as a PyTorch nn.Module from scratch, without calling nn.MultiheadAttention. Handle the reshape from (B, T, D) to (B, H, T, D_head), scaled dot-product scores, softmax, and the output projection.
  • How would you add L2 regularization to both linear and logistic regression, and what changes in the gradient update?
  • Why does scaling attention scores by the square root of the head dimension matter?

“They really do make you derive it first.”

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Uber·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Uber MLE interview with a dynamic programming problem on counting ways to reach a target score. Pretty standard bounded knapsack territory but still worth thinking through carefully before diving into code.
  • Given a list of question types where each type has a fixed number of available questions and a fixed point value per question, count the number of distinct ways to score exactly a target number of points. Return the result modulo 10^9 + 7.

“This is basically a bounded knapsack / DP counting problem.”

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Uber·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Uber ML engineer screen, just one coding question the whole time. Pretty straightforward problem on paper but I second-guessed myself more than I should have.
  • Given an integer array, find the index of the first local minimum. A local minimum is an element strictly smaller than its immediate neighbors, with edge cases handled for the first and last positions. Return -1 if none exists.

“I jumped straight to binary search because I'd seen similar problems before, then realized mid-explanation that the problem asks for the FIRST local minimum by index, not just any one.”

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Uber·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Uber ML engineer interview that was basically one big coding and design problem: build a next-word predictor from scratch, no libraries allowed. Felt like a reasonable test of fundamentals but the scope kept expanding as the conversation went on.
  • Build a probabilistic next-word generator from scratch without any NLP or ML libraries. Given a text corpus, implement tokenization, build n-gram counts, convert them to probabilities, handle unseen contexts, and sample from the resulting distribution.
  • What are the trade-offs between bigram, trigram, and higher-order n-gram models, and how would you extend this kind of system toward a neural approach?

“This started fine.”

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Uber·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Uber ML engineer interview that was basically a deep dive into logistic regression, not just the implementation but all the surrounding theory. More thorough than I expected for a single question.
  • Implement logistic regression from scratch for binary classification, including the sigmoid model, cross-entropy loss, gradient descent training, and both predict_proba and predict methods.
  • Why use cross-entropy loss instead of mean squared error for classification tasks?
  • How would you extend binary logistic regression to handle multi-class classification, and how would you add L2 regularization?
  • Walk through how you'd evaluate a binary classifier, and explain how changing the decision threshold affects your metrics.

“The coding part was fine.”

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Uber·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Uber MLE interview with a graph/BFS problem that looks deceptively clean on the surface. The bus routes question took me longer than I expected to untangle.
  • You're given a list of bus routes, where each route is a circular sequence of stops a bus repeats indefinitely. Starting at a given stop, find the minimum number of buses you need to board to reach a target stop. Return -1 if it's unreachable.

“My first instinct was Dijkstra and that was the wrong direction to go down.”

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Uber·Machine Learning Engineer·Onsite - Multi Round

Jul 2026
Uber ML Engineer interview that covered two pretty meaty areas: a deep dive into a system I'd actually built end to end, and a behavioral question about handling disagreements with teammates. Both felt like they wanted real stories, not textbook answers.
  • Walk me through an ML system you designed or owned from scratch. Cover the problem, business goal, data, features, model selection, training, evaluation, deployment, monitoring, and what impact it had.
  • How do you handle disagreements with teammates or cross-functional partners, especially around model choice, metrics, or product direction? Give a specific example.

“This is the kind of question that sounds easy until you're actually in it and realize you're trying to compress 18 months of work into 15 minutes without losing the thread.”

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Uber·Machine Learning Engineer·Onsite - Multi Round

Jul 2026
Went through the staff-level MLE loop at Uber, which is only two rounds: one focused on org scope and influence, the other a deep architecture walkthrough. The bar is genuinely different from senior; they're not looking for incremental improvements, they want you to show you redesigned the problem itself. Feedback after a rejection is basically nonexistent, so debrief yourself immediately.
  • Walk me through a project you led recently in detail, including the architectural decisions and how the project was scoped.
  • How do you measure your scope of influence at a staff engineering level, and how many teams or engineers have you actually impacted?
  • What technical direction did you set on this project versus what direction were you given?
  • Present a complex system you've built, covering the major subsystems, latency budget, model choices, and how failures are handled.
  • What were the failure modes in this system and how did you handle on-call incidents related to it?
  • What alternatives did you consider and reject when designing this architecture?
  • What do you expect from a staff-level engineer on this team, specifically?

“This is the core of the first round and it runs longer than you'd expect.”

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Uber·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Uber ML engineer interview with a pretty meaty algorithmic question about black-box function minimization. No gradient info allowed, just raw function evaluations. The question covered a lot of ground and I wasn't fully prepared for the integer domain twist.
  • You have a black-box function F(x) you can query at any real number x, and a search interval [a, b] where F is convex or unimodal. Design an algorithm to find an approximate minimizer x* within precision epsilon, using only function evaluations (no gradients, no derivatives). Cover the continuous case, the integer domain case, and analyze time and space complexity.

“This is basically ternary search, which I knew, but I stumbled when they pushed into the integer domain case.”

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