Uber·Product Manager·Onsite - Product Sense / Strategy
- How would you improve the passenger experience at a major metropolitan airport?
“This one is deceptively wide.”
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
“This one is deceptively wide.”
“Straightforward simulation once you see it.”
“The greedy insight is to think about the 'cost' of giving a task to worker 1 versus worker 2, specifically the difference reward1[i] minus reward2[i].”
“This is a two-proportion z-test setup and I knew that pretty quickly, but I fumbled the confidence interval formula under pressure.”
“The core question.”
“I went deep on the scoring model early, like way too deep.”
“My first instinct was to just DFS from every cell for every word, which works but completely falls apart when you have tens of thousands of words.”
“This one I actually felt okay about.”
“I jumped straight into supply-demand balancing and surge pricing adjustments, which in hindsight felt too obvious.”
“Backtracking felt straightforward and I got through it fine.”