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

Senior

Jul 2026

Summary

Applied Scientist interview at Microsoft with a meaty system design problem around real-time ML inference. One question, lots of depth required, and I left feeling like I'd only scratched the surface of what they were looking for.

Questions Asked (1)

Design a low-latency service that ingests a continuous stream of keystroke events (character, timestamp, cursor position) and predicts the user's current intent (e.g., writing a function, searching an API, refactoring code) in order to surface proactive suggestions in real time.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

I started with the streaming pipeline and spent probably too long on the ingestion layer before even touching the model side.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then propose a streaming architecture with a fast inference path and a slower model update path. Focus on latency budgets, model selection, and trade-offs between accuracy and speed.

Pro tip: Emphasize the importance of a feedback loop: capture user reactions to suggestions to continuously improve the model, and design for graceful degradation when the model is uncertain.

1. Clarify Requirements and Constraints

Ask about expected event rate, latency SLA, accuracy needs, and privacy constraints. Define what 'real-time' means (e.g., <100ms).

2. High-Level Architecture

Propose a pipeline: ingestion (Kafka/Kinesis) -> feature extraction -> model inference -> suggestion delivery. Include a separate path for model training/updating.

3. Low-Latency Inference Design

Discuss model choices (e.g., lightweight RNN/Transformer, ONNX runtime), batching, caching, and edge vs. cloud deployment. Consider pre-computing features.

4. Scalability and Reliability

Address partitioning by user, load balancing, fault tolerance, and monitoring. Ensure low tail latency with techniques like hedged requests.

5. Trade-offs and Evaluation

Compare accuracy vs. latency, model size vs. inference speed, and cloud vs. edge. Discuss metrics: p99 latency, suggestion acceptance rate.

Key Points to Mention

Latency budget breakdown: ingestion, feature extraction, inference, delivery
Model selection: lightweight models (e.g., distilled transformers) and quantization
Stream processing frameworks: Kafka, Flink, or Kinesis for ingestion
Feature engineering: n-grams, cursor context, time since last event
Feedback loop: implicit signals (acceptance/rejection) for continuous learning
Privacy and security: on-device processing, data anonymization

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