Uber SDE Interview Experience (2025) – Complete Breakdown
If you’re aiming for an SDE role at Uber, you’re signing up for more than just LeetCode questions. Uber builds and runs massive real-time systems: trip matching, maps, surge pricing, Uber Eats, payments, safety features and a ton of internal tooling behind the scenes.
Because of that, the interview loop is designed to test three big things:
Can you code under pressure and solve non-trivial DSA problems?
Can you reason about systems – data flows, APIs, design trade-offs?
Do you work the way Uber works – ownership, speed, customer obsession, doing the right thing?
This blog walks through the typical SDE-1 style process (also similar for 0–2 YOE backend roles), and then shows you how to prepare round by round.
1. How the Uber SDE Interview Is Structured
2. Round 1 – Online Coding Assessment (OA)
3. Round 2 – Technical Coding Interview (DSA + Debugging)
4. Round 3 – System / Low-Level Design
5. Round 4 – Hiring Manager / Values & Collaboration
6. What Uber Looks for Overall
1. How the Uber SDE Interview Is Structured
The exact loop depends on location, team and level, but most recent SDE-1 experiences and guides converge to something like:
Round 1 – Online Coding Assessment (OA) – CodeSignal / HackerRank
Round 2 – Technical Coding Interview (DSA + problem-solving)
Round 3 – System / Low-Level Design (often for backend / full-time SDE-1)
Round 4 – Hiring Manager / Values & Collaboration
Some candidates see 3 rounds (when design is merged with coding / HM) and some see 5 rounds (extra coding or bar-raiser style behavioral).
2. Round 1 – Online Coding Assessment (OA)
Most SDE and backend candidates start with an online assessment, usually on CodeSignal (sometimes HackerRank).
What it usually looks like
Platform: CodeSignal
Duration: ~60–70 minutes
Questions: 3–4 problems
Difficulty: Medium to medium-hard
Recent reports mention topics like:
Arrays & strings (prefix/suffix sums, sliding window)
Binary search patterns
1D / 2D DP
Graph-style problems (paths, constraints, or queues with conditions)
The questions are not pure “copy from LeetCode” but feel very similar in style and difficulty.
How you’re evaluated
Number of test cases passed (correctness)
Performance on large inputs (time & space)
Sometimes partial scoring for partially correct solutions
How to prep
Grind medium-level LeetCode in: arrays, strings, DP, graphs, sliding window.
Practise writing full solutions in one go: input parsing, edge cases, complexity explanation.
Simulate 60-minute mocks with 3 questions back-to-back to get used to the pressure.
3. Round 2 – Technical Coding Interview (DSA + Debugging)
If you clear the OA, you’ll hit 1–2 live coding rounds with an engineer. These could be virtual (video + shared editor) or on-site.
Typical structure
Duration: 45–60 minutes
Questions: 1–2 problems with follow-ups
Focus: DSA, clean code, communication
Common patterns
Based on recent SDE-1 and SDE-2 reports:
Graph / shortest-path / scheduling-style problems
Sliding window / two pointers / prefix-sum tricks
Trees & tries, heaps, or queue-based BFS/DFS
DP on arrays/strings (min cost / max value / counting paths)
Occasionally some debugging of existing code or handling edge cases
What interviewers are watching
Do you start with a brute-force and then optimise, or just guess at an optimal solution?
Do you talk through your approach and ask clarifying questions?
Do you write code that’s structured and readable, or a single giant function?
Do you test your own code with corner cases (empty input, single element, maximum constraints)?
Preparation ideas
Practise solving one medium-hard problem in 25–30 minutes while explaining out loud.
For every problem you solve, explicitly write:
Brute-force idea
Optimised approach
Complexity analysis
Get used to thinking in patterns – sliding window, BFS, Dijkstra, interval problems, etc.
4. Round 3 – System / Low-Level Design
For full-time SDE-1 and especially backend roles, many loops include a system design or low-level design (LLD) round.
This round checks whether you can design components and services that would realistically run at Uber scale.
What the round looks like
Duration: 60–75 minutes
Format: 1 design exercise + follow-up questions
Level: High-level system design or detailed LLD (class design + data modelling)
Typical prompts
Guides and interview write-ups mention:
Design trip-matching service (riders ↔ drivers)
Design ETA / surge-pricing service
Design notification service for trip lifecycle events
Or general systems like rate limiter, feature flag service, room-reservation, etc.
What they expect you to cover
Requirements: Functional + non-functional (latency, availability, consistency, scale).
API design: Endpoints, request/response format, error handling.
Core entities & data models: How rides, drivers, locations, users, payments, etc. are stored.
High-level architecture: Services, databases, caches, queues, load balancers.
Trade-offs: Consistency vs availability, push vs pull, SQL vs NoSQL, partitioning keys.
Reliability: Failure scenarios, retries, idempotency, monitoring & alerts.
How to prep for this
Start with simple design problems: URL shortener, notification service, rate limiter.
Then move to Uber-style problems: trip-matching, ride-tracking, surge pricing.
For every design, force yourself to answer:
How does data flow through the system?
What happens if this component fails?
Where do we cache?
What metrics would we monitor?
You don’t need FAANG-level “SDE-3 architecture”, but you do need to think beyond one service and one DB.
5. Round 4 – Hiring Manager / Values & Collaboration
This is where Uber checks: “Will this person actually thrive here?”
You’ll meet a manager or senior engineer and go deep into your past work + Uber’s values.
What they care about
Uber’s current values emphasise things like:
Customer obsession / trip obsessed
Go get it / bias for action
Stand for safety
Diversity & collaboration (“great minds don’t think alike”)
Do the right thing – even when it’s hard or slow
In practice, that translates into questions around:
Times you owned a problem end-to-end (not just “did your ticket”).
Handling ambiguous or messy situations (changing requirements, half-baked specs).
On-call / incident stories – things broke, what did you do, what changed afterwards?
Working with PMs, designers, ops, other teams when opinions didn’t align.
Learning from failure or a wrong decision – and how you corrected it later.
Great ways to stand out
Use 2–3 flagship project stories with metrics baked in:
Latency: “We brought p95 down from 800ms → 180ms.”
Reliability: “Reduced error rate from 2% → 0.3%.”
Scale: “Handled 10x more traffic with only 20% extra infra cost.”
Business: “Improved conversion by 4%” or “cut refund rate by 1.2%.”
Frame everything using STAR (Situation, Task, Action, Result), and explicitly call out:
Where you showed ownership
How you kept users/customers in mind
What you’d do differently now
6. What Uber Looks for Overall
If you zoom out from individual rounds, most resources and candidate stories point to the same big themes:
Strong DSA + coding fundamentals
You don’t need to be a grandmaster, but medium-hard problems in 30–40 minutes should be manageable.
Systems thinking
Understand how multiple services talk, where data lives, and how to reason about scale and failure.
Data-driven mindset
You care about metrics, not just “I think this is better.”
Alignment with values
Customer obsessed, act like an owner, move fast but responsibly, do the right thing.
Clear communication
Asking clarifying questions, thinking aloud, collaborating with the interviewer instead of “performing in silence”.