Uber Machine Learning Interview Questions
Practice the exact questions companies are asking right now.
"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."
"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."
"Got a Google recruiter call on Monday, interview on Friday. Crammed PracHub for 4 days. Passed every round. This platform is a miracle worker."
"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."
"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."
"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."
"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."
"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."
"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."
"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."
"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."
"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."
Implement convex minimization on an interval
Task: Minimize a Black-Box Convex Function on [a, b] Using Only Function Evaluations Context You are given a real-valued, convex function F(x) defined...
Diagnose location-sorted recommender causing revenue drop
Eats recommendations were changed to rank items primarily by distance to the user; after launch, add-to-cart rate rose but revenue per session fell. D...
Design ETA prediction for Uber rides
System Design: Real‑Time Pickup and Drop‑off ETA Prediction Context: You’re designing an end‑to‑end system that predicts pickup and drop‑off ETAs at t...
Build and assess CTR prediction
CTR Prediction with Delayed Feedback and Extreme Class Imbalance You are building a model to predict the probability that an ad impression results in ...
Build and deploy an uplift targeting model
Uplift Modeling and Policy Design for Free Trial/Bonus Targeting You ran a past randomized test that offered some users a free trial/bonus (treatment)...
Implement 1D convex minimization in Python
1D Black-Box Convex Minimization (Gradient-Free) Task Implement in Python an algorithm to minimize a 1D convex function F(x) over a closed interval [a...
Optimize Surge Notifications for Rideshare Drivers
Scenario A rideshare marketplace experiences airport demand spikes. When demand exceeds supply, the system can send surge-pricing push notifications t...
Select the better $5 promo-targeting model
Coupon Targeting Under a Daily Budget: Policy, OPE, Calibration, and Monitoring Context - You have two user-scoring models for a $5 coupon: M0 (curren...
Explain XGBoost depth, regularization, and dropout
ML Conceptual Questions (Onsite) Answer the following: (a) Gradient-boosted decision trees: How does maximum tree depth affect bias/variance, overfitt...