Uber·Product Manager·Onsite - Product Sense / Strategy
- You're the PM for UberPool. How would you create value for it?
“I went straight to rider savings and driver utilization, which felt obvious in retrospect.”
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
“I went straight to rider savings and driver utilization, which felt obvious in retrospect.”
“The core idea clicked pretty fast.”
“I started with the obvious stuff like ID verification failures and age restrictions, then got into things like driver refusals and stricter delivery windows.”
“I had a decent story ready but fumbled the delivery.”
“I jumped straight into the data ingestion layer and kind of forgot to nail down what kind of heatmap we were even talking about.”
“I jumped straight to BFS on a directed graph, multiplying edge weights along the path.”
“I started with brute force, picked an arbitrary root, ran a DFS to count reversals, then tried to re-run for every possible root.”
“Pretty standard opener but I still fumbled the 'why it mattered' part.”
“My first instinct was to just walk up the path and count character frequencies, which works but is way too slow if you think about the constraints.”
“I went straight into the data model and probably should've asked more clarifying questions first.”