Uber Interview Questions — Page 46

Uber·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Round 2 for an MLE role at Uber. Mixed coding and a case study in the same session, which I didn't expect. Interviewer was relaxed and easy to talk to, made the whole thing feel less high-stakes than it probably was.

Given access to a callable PDF function for a normal distribution, implement the CDF.
Walk through a promotion/coupon experiment for a food delivery platform: what metrics would you track, how would you randomize, and what confounders should you worry about?

“This is basically a numerical integration problem.”

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Uber·Data Scientist·Technical Phone Screen

Jun 2026

Uber Data Scientist interview with a fairly involved SQL scenario built around an A/B test. One multi-part question covering joins, filtering, window functions, and conditional aggregation. The kind of problem that feels manageable until you realize how many moving pieces they're actually asking for.

Given three tables tracking campaign assignment, user sessions, and orders, write SQL to compute average revenue per user for test vs. control groups, restricted to January 2024 sessions in the SF region. Then, for each treatment arm, return the top three users by order count. Finally, calculate the percentage of users in each arm who placed more than five orders.

“This one had layers.”

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

Jun 2026

Uber MLE system design round focused entirely on building a personalized feed ranking system from scratch. The scope was pretty wide and they clearly wanted to see if you could hold the full pipeline in your head at once.

Design a personalized feed ranking system that returns an ordered list of content items from a massive corpus, tailored to each user based on their history, interests, social connections, and content freshness.
How would you handle multiple competing objectives in the ranker, like engagement, time spent, content diversity, and content integrity?
Walk through how you'd support A/B testing and fast model iteration in a feed ranking system at scale.
What are the latency and scalability constraints you'd design around, and how do they influence your architecture choices?

“This is a big one.”

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Uber·Machine Learning Engineer·Technical Phone Screen

Jun 2026

ML Engineer interview at Uber focused almost entirely on inference optimization and ML infra depth. They started with modeling fundamentals to warm up, then went hard on concrete work you've done, specific techniques, and measurable results. Pretty technical throughout.

Walk me through your hands-on experience with ML inference optimization. What models or workloads have you worked on and what techniques did you apply?
What bottlenecks did you identify in your inference pipeline, and how did you measure them?
What end-to-end latency and throughput improvements did you deliver, and what were the trade-offs involved?
How does your ML optimization work intersect with infrastructure concerns like scheduling, autoscaling, and accelerator selection?
Which serving stacks have you worked with, like vLLM, TensorRT-LLM, or Triton, and what drove your choice between them?
What are the fundamentals of how a transformer model runs at inference time, and where are the common performance failure points?

“This was basically the whole interview compressed into one question.”

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Uber·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at Uber for a software engineering role. The whole session was focused on designing a heatmap system, which sounds straightforward until you get into the tile pyramid and real-time aggregation stuff. Walked away feeling like I covered the basics but probably left some depth on the table with the trade-off discussion.

Design a heatmap system that ingests user activity events (with coordinates and weights), serves rendered tiles at different zoom levels, and supports both real-time and historical views.
What are the trade-offs between precomputing heatmap tiles versus computing them on demand?
How would you handle the aggregation pipeline for this system, and what retention policy would you apply to the event data?

“I started with the ingestion side because that felt most concrete to me.”

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Uber·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Uber ML engineer interview that went pretty deep on the fundamentals. The question sounds straightforward until you're actually writing out gradient derivations on the spot and trying to remember the difference between BCE and MSE loss without Googling anything.

Implement linear regression and logistic regression from scratch, covering the prediction function, loss function, gradient computation, gradient descent training, regularization, and when you'd use one over the other.

“This is one of those questions that feels like a gimme until you're 10 minutes in and fumbling through the chain rule for logistic loss.”

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Uber·Software Engineer·Technical Phone Screen

Jun 2026

Uber SWE interview with a tree traversal problem that sounds manageable until you actually sit down and think through all the edge cases. Not the worst coding round I've had but definitely not a gimme.

Given the root of a binary tree, return the vertical order traversal as a list of lists. Each node has a (row, col) position, children shift one column left or right, and ties within the same row and column are broken by node value.

“I got the basic structure down pretty quick, grouping nodes by column using a map.”

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Uber·Software Engineer·Onsite - Coding / Algorithms

Jun 2026

Uber coding round with a tricky seat assignment problem I hadn't seen before. Couldn't crack it and I'm still not sure there's a clean LeetCode equivalent for it.

Design a class that assigns seats to employees one at a time, where each new assignment maximizes the minimum distance from all previously occupied seats. The class takes a total seat count and exposes a single assign method.

“Didn't solve it.”

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Uber·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

PM interview at Uber, product strategy question about a quick-commerce competitor. One question, product sense heavy, felt like a case study more than a typical PM screen.

As a PM for a rapid grocery delivery app, how would you build a strategy to grow the average order value per user?

“I went straight to segmenting users by order frequency and spend, which felt right, but I spent too long on the analysis setup and not enough on actual levers.”

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Uber·Data Scientist·Onsite - Behavioral / Leadership

Jun 2026

Prepped for a data scientist role at Uber and ran through a set of behavioral and technical questions that felt pretty representative of what they actually care about. The dynamic demand project question was the one I was most nervous about since it requires you to hold a lot in your head at once.

Tell me about a time you went above and beyond what was expected of you.
Describe a situation where you disagreed with your team, the outcome didn't go your way, and what you took away from it.
Walk me through a project you worked on involving dynamic demand forecasting or a similar problem. Cover the business context, your technical choices, any tradeoffs you made, how you worked with stakeholders, and what the actual impact was.

“Solid question to prep but easy to fumble if your example is too vague.”

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