Candidates who clear Uber loops consistently describe preparing DSA, machine coding / LLD, HLD, and behavioral prep as separate tracks, with machine coding treated as its own skill — writing fully runnable code under time pressure — rather than folded into general DSA practice.
A track-by-track roadmap
DSA: Uber leans on dynamic programming, graphs, and shortest-path patterns, often with a live follow-up that changes the constraints mid-round. Work through the DSA Interview Questions here, and keep your core data-structure fundamentals sharp.
Machine coding / LLD: Practice going from a vague prompt straight to a fully working implementation — Uber interviewers consistently reward reaching a runnable end-to-end state quickly over a polished but incomplete design. Start with the LLD Interview Questions here.
HLD: Structure every answer the same way — requirements, data model, the core algorithmic piece (debt simplification, geohashing, windowed stream processing), then scaling — rather than jumping straight to boxes and arrows. See the System Design Interview Questions.
Behavioral / culture-fit: Prepare 2–3 real stories that can each answer multiple themes, in STAR format — especially useful when a loop splits behavioral content across two separate hiring-manager rounds. See Behavioral & Culture-Fit Questions.
One recurring piece of advice across candidate reports: get a machine-coding solution to a runnable state first, then use remaining time for edge cases and debugging — a polished but incomplete design tends to score worse than a rough but working one.
Read real, round-by-round Uber Interview Experiences to see how this plays out in an actual loop.