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

Senior

Apr 2026

Summary

Applied Scientist interview at Microsoft with a system design question focused on building a real-time code completion service at massive scale. Pretty intense single-question format that pushed hard on latency and throughput constraints.

Questions Asked (1)

Design a service that accepts a code context of up to 1,000 tokens and returns the top 5 completion suggestions within 100 milliseconds, at a scale serving millions of concurrent developers.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

The latency constraint is what trips you up if you're not careful.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a high-level architecture that separates concerns: a low-latency inference service, a scalable caching layer, and a distributed model serving infrastructure. Focus on trade-offs between latency, accuracy, and cost, and discuss how to achieve 100ms p99 latency at scale using techniques like model quantization, caching, and edge deployment.

Pro tip: Emphasize the importance of measuring and optimizing for p99 latency, not just average, and discuss how to handle tail latency through techniques like hedged requests and adaptive timeouts. Also, mention the need for a feedback loop to continuously improve model quality based on user interactions.

1. Clarify Requirements and Constraints

Ask questions to understand the expected quality of suggestions, the acceptable latency (p50 vs p99), the scale (millions of concurrent developers), and the cost constraints. Clarify whether the service is for code completion in an IDE or a cloud-based API.

2. High-Level Architecture

Propose a layered architecture: an API gateway for load balancing and rate limiting, a caching layer for frequent contexts, a model serving layer with multiple instances, and a fallback mechanism for when the model is slow or unavailable.

3. Model and Inference Optimization

Discuss model choices (e.g., transformer-based models) and optimization techniques such as quantization, pruning, and knowledge distillation to reduce inference time. Consider using a smaller model for latency-critical paths and a larger model for offline or asynchronous improvements.

4. Scaling and Latency Management

Explain how to scale horizontally using container orchestration (e.g., Kubernetes) and autoscaling. Use techniques like request hedging, speculative execution, and caching of frequent code contexts to meet the 100ms latency target. Discuss geographic distribution and edge deployment to reduce network latency.

5. Monitoring, Feedback, and Iteration

Describe how to monitor latency, throughput, and model accuracy in real-time. Implement A/B testing and a feedback loop to collect user acceptance of suggestions to continuously improve the model. Discuss how to handle failures gracefully with fallbacks to simpler models or cached results.

Key Points to Mention

Latency budget breakdown: network, preprocessing, model inference, postprocessing, and how to allocate the 100ms.
Caching strategies: exact match caching, semantic caching, and using a key-value store like Redis with low-latency access.
Model serving frameworks: TensorFlow Serving, TorchServe, or custom solutions with gRPC for efficient communication.
Load balancing and autoscaling: using consistent hashing, least connections, and predictive autoscaling based on traffic patterns.
Trade-offs between model size and latency: using smaller models or early exit strategies for fast responses.
Fault tolerance and graceful degradation: fallback to a simpler model or cached suggestions when the primary model is slow or unavailable.

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