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

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May 2026

Summary

System design round at Qualcomm for a Research Scientist role, focused entirely on distributed training infrastructure for large models. Pretty intense scope for a single question but they clearly wanted to see how deep you could go.

Questions Asked (1)

Design a distributed training system for large deep learning models, covering parallelism strategies, gradient synchronization, fault tolerance, communication topology, and how to scale to hundreds of GPUs.

System DesignTechnical Trade-offs

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 constraints, then propose a hybrid parallelism strategy (data, tensor, pipeline) tailored to model size and hardware. Walk through the system architecture covering communication, synchronization, fault tolerance, and scaling, and discuss trade-offs at each layer.

Pro tip: Emphasize that communication is often the bottleneck; propose overlapping computation with communication and using hierarchical all-reduce to minimize latency. Also, mention that fault tolerance should be designed with checkpointing and elastic training to handle failures gracefully.

1. Clarify Requirements and Constraints

Ask about model size, dataset size, hardware (GPU type, interconnect), and training time budget to scope the design.

2. Choose Parallelism Strategies

Decide on data, tensor, and pipeline parallelism based on model architecture and memory constraints; explain how they combine.

3. Design Communication and Synchronization

Detail gradient synchronization methods (all-reduce, ring, tree), communication topology (NVLink, InfiniBand), and overlap techniques.

4. Implement Fault Tolerance and Checkpointing

Describe checkpointing strategies, failure detection, recovery, and elastic scaling to handle node failures.

5. Address Scaling and Trade-offs

Discuss how the system scales to hundreds of GPUs, including bottlenecks, efficiency, and trade-offs between strategies.

Key Points to Mention

Hybrid parallelism: combining data, tensor, and pipeline parallelism for large models
Gradient synchronization: all-reduce algorithms (ring, tree), hierarchical all-reduce, and communication overlap
Communication topology: NVLink, InfiniBand, and network topology awareness for efficient collectives
Fault tolerance: asynchronous checkpointing, elastic training, and recovery mechanisms
Scaling to hundreds of GPUs: batch size scaling, learning rate adjustment, and communication bottlenecks
Trade-offs: memory vs. communication, throughput vs. latency, and complexity vs. performance

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.