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

Senior

May 2026

Summary

Applied Scientist interview at Microsoft centered on a pretty gnarly system design problem around LLM output formatting. One round, one meaty question, and a lot of pressure to think through both latency and scale at the same time.

Questions Asked (1)

Design a service that takes raw text output from a large language model and transforms it to comply with Microsoft Word formatting standards, including styles, headings, tables, and footnotes. The system needs to handle low-latency interactive editing and high-throughput batch document generation.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This one has two very different performance profiles baked into a single system and I didn't separate them clearly enough at first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a modular architecture that separates parsing, transformation, and rendering. Address both low-latency interactive editing and high-throughput batch generation by discussing trade-offs and optimizations for each mode.

Pro tip: Emphasize idempotency and incremental updates for interactive editing, and leverage Microsoft's existing libraries (e.g., Open XML SDK) to avoid reinventing the wheel. Show awareness of cost and performance implications of LLM output variability.

1. Clarify Requirements and Constraints

Ask about expected input formats, output fidelity, latency targets, throughput requirements, and integration points with Microsoft Word. Confirm whether the service is a library, API, or standalone tool.

2. Design Core Components

Outline a pipeline: LLM output parser, intermediate representation (IR), style mapper, and Word document generator. Use Open XML SDK for .docx manipulation and consider a rule-based or ML-based approach for mapping text to styles.

3. Address Interactive Editing

For low-latency editing, propose incremental parsing and rendering, caching of parsed structures, and optimistic UI updates. Discuss how to handle partial LLM outputs and real-time user edits.

4. Address Batch Generation

For high throughput, suggest parallel processing, batching of LLM outputs, and asynchronous job queues. Optimize by reusing templates and pre-compiling styles.

5. Discuss Trade-offs and Scalability

Compare monolithic vs. microservices, synchronous vs. asynchronous processing, and accuracy vs. performance. Mention monitoring, error handling, and extensibility for new Word features.

Key Points to Mention

Use of Open XML SDK for Word document generation and manipulation
Intermediate representation (IR) to decouple parsing from rendering
Incremental parsing and caching for low-latency interactive editing
Parallelization and job queues for high-throughput batch processing
Handling LLM output variability and error correction
Trade-offs between accuracy, latency, and cost

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