Uber Software Development Engineer Interview Questions (2026)

1. 1Recruiter Screen30 minEasy

   Role targeting, level calibration and process expectations.

2. 2Technical Phone Screen60 minMedium

   One or two medium DSA problems (CodeSignal or live) with emphasis on correct, runnable code and edge cases.

3. 3Onsite Coding I60 minHard

   Practical problem such as building a rate limiter or an in-memory index, judged on working code and API cleanliness.

4. 4Onsite Coding II60 minHard

   Algorithmic problem often with a geospatial or streaming flavor, pushed to optimal complexity.

5. 5System Design60 minHard

   Design a real-time marketplace system (dispatch, ETA, surge) with hard follow-ups on scale, geo-sharding and failure modes.

6. 6Behavioral / Hiring Manager Round45 minMedium

   STAR stories mapped to Uber's cultural norms: ownership, bold bets, customer obsession and conflict handling.

1. Q1

   For Uber's trip receipt generation flow, write production-quality backend code to build an LRU cache with TTL and hit/miss metrics while meeting zero-downtime deployment. Describe the main function or class interface, the core data structures, and the edge cases you would test

   MediumCodingCaching

   How to answer:

   A strong answer proposes a small, testable interface and uses hash map plus doubly linked list or ordered dictionary with lazy expiry. It handles retries, invalid input, timeouts, and cleanup explicitly, and states O(1) expected get/put.

2. Q2

   For Uber's driver incentives flow, write production-quality backend code to implement stale-while-revalidate with request coalescing while meeting limited memory per worker. Describe the main function or class interface, the core data structures, and the edge cases you would test

   MediumCodingCaching

   How to answer:

   A strong answer proposes a small, testable interface and uses serve fresh or bounded-stale values and allow one refresh per key. It handles retries, invalid input, timeouts, and cleanup explicitly, and states O(1) cache lookup with bounded refresh fanout.

3. Q3

   Design cache-aware read APIs with explicit cache-control semantics and invalidation hooks for Uber's driver incentives read/write workflow. Specify endpoints or RPCs, request and response schemas, error handling, authentication, idempotency, and pagination where relevant

   MediumAPI DesignCaching

   How to answer:

   A strong answer defines resource-oriented REST endpoints or clear RPC methods for create, retrieve, update, list, and audit operations. It includes stable identifiers, authorization boundaries, validation rules, explicit error codes, rate limits, and backward-compatible versioning.

4. Q4

   Design a eventually consistent workflow for Uber's trip receipt generation using multi-layer cache hierarchies, invalidation fanout, hot-key handling, and stale reads. The system must handle 50 million daily active users, limited memory per worker, and schema evolution mismatch. Explain architecture, data flow, consistency, and operations

   HardDistributed SystemsCaching

   How to answer:

   A strong answer decomposes the system into stateless frontends, partitioned services, durable storage, and async processing. It justifies idempotent commands, sagas, retries, compensation, and reconciliation, uses idempotency and back-pressure, and defines SLOs, alerts, and rollback mechanisms.

5. Q5

   Design a transactional schema for Uber's trip receipt generation with emphasis on cache metadata, invalidation logs, materialized views, and read-through/write-through tradeoffs. Include tables or collections, keys, indexes, consistency guarantees, and a migration strategy

   MediumDatabase DesignCaching

   How to answer:

   A strong answer starts from access patterns, then proposes entities, primary keys, foreign keys, uniqueness constraints, and transaction boundaries. It explains transaction scope, isolation level, retention needs, and how the model handles growth to 50 million daily active users.

6. Q6

   Design a query-optimized schema for Uber's driver incentives with emphasis on cache metadata, invalidation logs, materialized views, and read-through/write-through tradeoffs. Include tables or collections, keys, indexes, consistency guarantees, and a migration strategy

   HardDatabase DesignCaching

   How to answer:

   A strong answer starts from access patterns, then proposes compound indexes, denormalized read models, pagination, and query-plan validation. It explains transaction scope, isolation level, retention needs, and how the model handles growth to 1 billion stored records.

7. Q7

   Tell me about a time you improved reliability involving handling disagreement about cache freshness, correctness, or invalidation strategy in a context like Uber's ride matching. Use a concrete example and explain your role, actions, tradeoffs, and results

   EasyBehavioralCaching

   How to answer:

   A strong answer uses the STAR format, names the technical problem, explains the candidate's specific ownership, and quantifies impact. It should cover incident response, root cause analysis, and measurable reliability improvement and show learning rather than blame.

8. Q8

   Tell me about a time you influenced a technical decision without authority involving handling disagreement about cache freshness, correctness, or invalidation strategy in a context like Uber's trip receipt generation. Use a concrete example and explain your role, actions, tradeoffs, and results

   MediumBehavioralCaching

   How to answer:

   A strong answer uses the STAR format, names the technical problem, explains the candidate's specific ownership, and quantifies impact. It should cover tradeoff analysis, stakeholder alignment, and decision documentation and show learning rather than blame.

9. Q9

   Tell me about a time you handled a production failure or missed deadline involving handling disagreement about cache freshness, correctness, or invalidation strategy in a context like Uber's driver incentives. Use a concrete example and explain your role, actions, tradeoffs, and results

   HardBehavioralCaching

   How to answer:

   A strong answer uses the STAR format, names the technical problem, explains the candidate's specific ownership, and quantifies impact. It should cover ownership, communication, mitigation, and follow-up prevention and show learning rather than blame.

10. Q10

    Design asynchronous job APIs with status, cancellation, leases, and safe concurrent updates for Uber's fraud detection bulk ingestion workflow. Specify endpoints or RPCs, request and response schemas, error handling, authentication, idempotency, and pagination where relevant

    HardAPI DesignConcurrency

    How to answer:

    A strong answer defines resource-oriented REST endpoints or clear RPC methods for batch submission, partial failure reporting, asynchronous status, and replay. It includes stable identifiers, authorization boundaries, validation rules, explicit error codes, rate limits, and backward-compatible versioning.

11. Q11

    Design a highly available regional service for Uber's surge pricing using distributed locks, leases, fencing tokens, idempotent operations, and contention management. The system must handle petabyte-scale logs, bursty traffic during launches, and a thundering herd. Explain architecture, data flow, consistency, and operations

    HardDistributed SystemsConcurrency

    How to answer:

    A strong answer decomposes the system into stateless frontends, partitioned services, durable storage, and async processing. It justifies partitioning, replication, leader election or quorum decisions, and regional failover, uses idempotency and back-pressure, and defines SLOs, alerts, and rollback mechanisms.

12. Q12

    Design a global low-latency platform for Uber's ride matching using distributed locks, leases, fencing tokens, idempotent operations, and contention management. The system must handle peak holiday traffic, tenant isolation, and schema evolution mismatch. Explain architecture, data flow, consistency, and operations

    ExpertDistributed SystemsConcurrency

    How to answer:

    A strong answer decomposes the system into stateless frontends, partitioned services, durable storage, and async processing. It justifies multi-region routing, data locality, cache hierarchy, and conflict resolution, uses idempotency and back-pressure, and defines SLOs, alerts, and rollback mechanisms.

13. Q13

    Design a migration and retention plan for Uber's trip receipt generation with emphasis on isolation levels, row locks, optimistic versioning, and contention-safe updates. Include tables or collections, keys, indexes, consistency guarantees, and a migration strategy

    HardDatabase DesignConcurrency

    How to answer:

    A strong answer starts from access patterns, then proposes online migrations, backfills, archival, data validation, and rollback. It explains transaction scope, isolation level, retention needs, and how the model handles growth to 10k requests per second.

14. Q14

    Uber's ride matching has grown to peak holiday traffic. Propose a capacity plan focused on bounded queues, lock contention, work stealing, fairness, and saturation control while preserving bursty traffic during launches. Explain bottlenecks, tradeoffs, instrumentation, and rollout

    HardScalabilityConcurrency

    How to answer:

    A strong answer quantifies traffic first, then targets the highest-risk bottleneck with traffic estimates, CPU and memory sizing, database QPS, and dependency budgets. It uses staged rollout, load tests, dashboards, and fallback behavior rather than only adding more machines.

15. Q15

    Uber's driver incentives has grown to 100k events per second. Propose a resilience and cost plan focused on bounded queues, lock contention, work stealing, fairness, and saturation control while preserving tenant isolation. Explain bottlenecks, tradeoffs, instrumentation, and rollout

    ExpertScalabilityConcurrency

    How to answer:

    A strong answer quantifies traffic first, then targets the highest-risk bottleneck with autoscaling, graceful degradation, SLOs, error budgets, and cost controls. It uses staged rollout, load tests, dashboards, and fallback behavior rather than only adding more machines.

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Strong DSA and scalable system design; prepare analytical/behavioral stories

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