Uber interview notes, page 61 of 61

Large Enterprises

Most of these Uber write-ups come from Software Engineers (407), then Data Scientists (78) and Machine Learning Engineers (65). The most reported rounds are the technical phone screen (387), the system design round (95), the product sense round (46) and the onsite coding round (23).

609 interview notes · updated Aug 2026

Uber·Data Scientist·Technical Phone Screen

Jul 2026

SQL-heavy technical screen for a Data Scientist role at Uber. Two tables, one prompt, and a pile of edge cases that made me realize I'd been sloppy about age calculations for years.

Given a drivers table and a trips table, write a query to return driver names whose average fare across completed trips is strictly greater than $10.00. Exclude canceled trips and null fares. Output driver name and average fare rounded to 2 decimal places, ordered by average fare descending then name ascending. Drivers with no completed trips should not appear.
Using the same schema, count completed trips grouped by driver age bands (20 to 35 and 36 to 45) as of a specific reference date. Age must be calculated accurately using the actual calendar, not a 365-day approximation. Ignore drivers outside those two bands.

“Felt pretty confident here.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026

Product sense interview for a Data Scientist internship at Uber. One big open-ended question that basically asked me to design a new rider feature from scratch, including metrics, experimentation, and marketplace tradeoffs. Pretty intense for an intern role.

Given that the Uber rider app already has core features like booking, ETA, fare estimates, and scheduling, propose one genuinely new feature that improves rider experience or marketplace efficiency. Walk through the target user, business case, success metrics, key tradeoffs, how you'd run an experiment, what segments you'd look at, and how you'd separate real impact from noise like novelty effects or seasonality.

“This was basically the whole interview in one prompt.”

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Uber·Software Engineer·Technical Phone Screen

Jul 2026

Uber SWE coding round, just the one grid traversal problem. Pretty standard BFS territory but the constraints are large enough that you can't be sloppy about it.

Given an m x n grid of open cells (0) and walls (1), find the shortest path in steps from the top-left to the bottom-right cell moving only up/down/left/right. Return -1 if no path exists.

“Classic BFS and I knew it immediately, which was both good and bad.”

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Uber·Software Engineer·Onsite - System Design / Architecture

Jul 2026

Uber system design round for a software engineering role, focused entirely on a distributed data pipeline problem. The question was dense and technical, covering a lot of ground in one shot.

You have a massive log of meeting events with fields like user ID, start/end time, room ID, and office ID. Design a MapReduce or Spark job to compute, for each rolling 15-minute window: the number of concurrent meetings per office, and which rooms exceeded 80% utilization. Walk through your map keys and values, how you handle interval-to-window transformation, shuffle and partition strategy to avoid skew, handling of late or duplicate events, and how you'd validate the output at scale.

“This one hit me like a wall.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026

Uber data science interview with a meaty end-to-end design question about the map search experience. One question, lots of surface area, and I definitely underestimated how deep they wanted to go on the ranking and experimentation side.

Design the full product and data science system behind a map application's address search bar, covering candidate generation, ranking, handling of fuzzy inputs and typos, location-aware results, relevance and popularity signals, metrics, experimentation, and failure modes.

“This question is deceptively wide.”

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Uber·Data Scientist·Onsite - System Design / Architecture

Jul 2026

Uber DS interview focused entirely on a deep system design problem around ETA prediction. The scope was massive and I felt like I was constantly playing catch-up trying to cover all the angles they wanted.

Design a full end-to-end system to predict pickup and drop-off ETAs at the time a ride is requested. Walk through your data sources, modeling choices, uncertainty handling, training and serving infrastructure, evaluation strategy, and how you'd roll it out.

“This was basically seven questions duct-taped into one.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026

Uber data scientist interview with a pretty brutal causal inference question about a messy real-world A/B test. The kind of problem where you think you know the framework and then realize halfway through that there are like three simultaneous complications to untangle.

You ran an A/B test on surge recommendations sent to drivers, but 30% of treated drivers ignored the suggestion and surge in one zone spills over into neighboring zones. How would you estimate the causal effect on completed trips, and what analysis plan would you use to handle both the noncompliance and the interference?

“This one took me a minute to even parse.”

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Uber·Software Engineer·Technical Phone Screen

Jul 2026

Uber SWE interview, coding round. One problem the whole time, stack-based simulation. Felt okay about my solution but the timestamp math tripped me up more than I expected.

Given execution logs from a single-threaded CPU running n functions, where each log entry records a function id, whether it started or ended, and a timestamp, compute the exclusive CPU time for each function (time the function itself ran, not counting time spent in functions it called).

“My first instinct was right, use a stack to track which function is currently running.”

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Uber·Software Engineer·Technical Phone Screen

Jul 2026

Uber coding screen for a software engineer role. One algorithmic problem, simulation-style, the kind that looks approachable until you're actually implementing it.

Given an m x n matrix of integers where equal values represent the same color, implement a single round of candy-crush style elimination: mark any cell for removal if at least 2 of its 4 neighbors share its value, set those cells to 0, then gravity-drop remaining values to the bottom of each column.

“The removal condition tripped me up at first.”

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