Meta Software Development Engineer Interview Questions (2026)

1. 1Recruiter screen30 minEasy

   Process overview, level calibration, and prep guidance — Meta recruiters actively coach on round formats.

2. 2Technical screen45 minHard

   Two DSA problems in 45 minutes on a plain shared editor with no autocomplete or execution.

3. 3Coding round ('Ninja')45 minHard

   Two more problems at loop difficulty; clean near-compilable code and verbalized complexity analysis expected.

4. 4System design ('Pirate')45 minHard

   Design a Meta-scale product system (feed, Stories, chat) with emphasis on read-heavy fan-out, caching, and data modeling.

5. 5Behavioral ('Jedi')45 minMedium

   Deep past-experience discussion on conflict, growth, and impact aligned to Meta values; graded as a real signal round.

1. Q1

   In a practical product design drill, answer this: Design classes for assigning Marketplace users to experiments

   MediumLow Level DesignA/B experiment SDK

   How to answer:

   Model Experiment, Variant, Allocation, EligibilityRule, AssignmentService, and ExposureLogger; use deterministic hashing and log exposures once. Optimize for simple, scalable product objects and fast iteration.

2. Q2

   In a fast 45-minute Meta-style coding round, solve this: Code a mapper from a raw Marketplace API response to a stable internal model

   HardCodingAPI response mapper

   How to answer:

   Validate required fields, provide defaults for optional fields, isolate external schema changes, and test unknown fields and backward compatibility. Keep the solution direct and code-ready under time pressure.

3. Q3

   In a fast 45-minute Meta-style coding round, solve this: Infer character order from sorted News Feed labels in an unknown alphabet

   HardDSAAlien dictionary

   How to answer:

   Build precedence edges from first differing characters, then topologically sort. O(total characters); detect invalid prefixes and cycles. Keep the solution direct and code-ready under time pressure.

4. Q4

   In a fast 45-minute Meta-style coding round, solve this: Write code to call multiple Groups services concurrently and return partial results safely

   MediumCodingAsync fanout

   How to answer:

   Use async tasks/futures with per-call timeouts, collect successes and errors separately, limit concurrency, and preserve result ordering if required. Keep the solution direct and code-ready under time pressure.

5. Q5

   In a fast 45-minute Meta-style coding round, solve this: Generate all valid configurations for a small Reels rule set with constraints

   MediumDSABacktracking combinations

   How to answer:

   Use DFS/backtracking, pruning invalid partial states early. Complexity is exponential; explain pruning and output-size limits. Keep the solution direct and code-ready under time pressure.

6. Q6

   In a fast 45-minute Meta-style coding round, solve this: Validate nested delimiters in a Threads expression language

   MediumDSABalanced parentheses

   How to answer:

   Use a stack of opening delimiters and match each closing delimiter. O(n), O(n); reject mismatches and leftover openings. Keep the solution direct and code-ready under time pressure.

7. Q7

   In a fast 45-minute Meta-style coding round, solve this: Find the first and last occurrence of a target in sorted Messenger results

   HardDSABinary search boundaries

   How to answer:

   Run two boundary binary searches for lower_bound and upper_bound-1. O(log n); test empty array and missing target. Keep the solution direct and code-ready under time pressure.

8. Q8

   For Meta's Reels recommendations flow, write production-quality backend code to normalize cache keys and add negative caching for not-found results while meeting a 150 ms p99 target. Describe the main function or class interface, the core data structures, and the edge cases you would test

   HardCodingCaching

   How to answer:

   A strong answer proposes a small, testable interface and uses canonical key construction, TTL tiers, and explicit invalidation hooks. It handles retries, invalid input, timeouts, and cleanup explicitly, and states O(1) key lookup with reduced origin load.

9. Q9

   In a Meta backend interview, solve this DSA problem from Instagram story delivery: choose objects for eviction under size and value constraints. Provide the algorithm, prove correctness, and analyze complexity under 100k events per second

   HardData Structures & AlgorithmsCaching

   How to answer:

   Model the problem with knapsack approximation or greedy by value/size depending on constraints. The target solution should achieve O(nW) exact pseudo-polynomial or O(n log n) greedy, handle empty inputs and ties, and explain why simpler brute-force approaches do not scale.

10. Q10

    Design cache-aware read APIs with explicit cache-control semantics and invalidation hooks for Meta's Reels recommendations 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.

11. Q11

    Design cache-aware read APIs with explicit cache-control semantics and invalidation hooks for Meta's Groups notifications bulk ingestion workflow. Specify endpoints or RPCs, request and response schemas, error handling, authentication, idempotency, and pagination where relevant

    HardAPI DesignCaching

    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.

12. Q12

    Design a highly available regional service for Meta's content moderation review queues using multi-layer cache hierarchies, invalidation fanout, hot-key handling, and stale reads. The system must handle 100k events per second, zero-downtime deployment, and slow database replicas. 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 partitioning, replication, leader election or quorum decisions, and regional failover, uses idempotency and back-pressure, and defines SLOs, alerts, and rollback mechanisms.

13. Q13

    Design a global low-latency platform for Meta's Reels recommendations using multi-layer cache hierarchies, invalidation fanout, hot-key handling, and stale reads. The system must handle 1 billion stored records, a 150 ms p99 target, and a thundering herd. Explain architecture, data flow, consistency, and operations

    ExpertDistributed SystemsCaching

    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.

14. Q14

    Design a query-optimized schema for Meta's Instagram story delivery 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.

15. Q15

    Tell me about a time you improved reliability involving handling disagreement about cache freshness, correctness, or invalidation strategy in a context like Meta's News Feed ranking. 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.

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