LinkedIn·Software Engineer·Onsite - System Design / Architecture·Senior

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Jul 2026

Summary

LinkedIn system design round for a software engineering role. The whole session was basically one giant distributed systems question that kept branching into sub-topics. Felt like I was defending a thesis by the end of it.

Questions Asked (1)

Design a distributed job execution system that runs many independent jobs in parallel across a cluster. Walk through the job model, APIs, how you partition work, scheduler and worker design, coordination, fault tolerance, idempotency, result aggregation, scaling, and consistency trade-offs.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This question is basically five questions stapled together.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then present a high-level architecture with a central scheduler, worker pool, and durable job queue. Walk through the job lifecycle from submission to result aggregation, emphasizing fault tolerance, idempotency, and trade-offs between consistency and availability.

Pro tip: Explicitly discuss idempotency and exactly-once semantics early, as they are critical for distributed job execution and often overlooked. Also, mention how you would monitor and handle stragglers to maintain performance.

1. Clarify Requirements and Scale

Ask about job types, expected throughput, latency requirements, and failure handling expectations. Establish assumptions about cluster size and job characteristics.

2. Define Job Model and APIs

Describe the job representation (e.g., DAG or independent tasks), submission API, status query, and result retrieval. Include idempotency keys for job submission.

3. Design Scheduler and Worker Architecture

Explain how the scheduler partitions work, assigns tasks to workers, and handles coordination. Discuss worker registration, heartbeats, and task leasing.

4. Address Fault Tolerance and Idempotency

Detail mechanisms for detecting failures, retrying tasks, and ensuring idempotent execution. Cover exactly-once semantics via deduplication and transactional writes.

5. Discuss Scaling, Consistency, and Trade-offs

Explain how the system scales horizontally, the consistency model chosen (e.g., eventual consistency for results), and trade-offs between availability and consistency.

Key Points to Mention

Job partitioning strategies (e.g., sharding by job ID, consistent hashing)
Scheduler design: centralized vs. distributed, use of a durable queue (e.g., Kafka)
Worker coordination: heartbeats, leases, and failure detection
Idempotency: idempotent job execution and exactly-once semantics
Result aggregation: handling partial results and final output
Scaling and consistency trade-offs: CAP theorem, eventual consistency

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