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Google·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

Senior
Jun 2026

Summary

System design round at Google for an ML engineer role. One big open-ended question about building an experiment tracking platform, which sounds straightforward until you realize how many directions it can go.

Questions Asked (1)

Q1

Design an ML experiment tracking and analysis platform.

System DesignTechnical Trade-offsData Modeling
Author's notes

I started with the basics: logging runs, storing hyperparameters, tracking metrics over time.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then propose a high-level architecture that separates metadata tracking, artifact storage, and analysis services. Focus on data modeling for experiments, runs, and metrics, and discuss trade-offs in storage, query performance, and scalability.

Pro tip: Emphasize how your design handles the unique challenges of ML workflows, such as reproducibility, lineage, and comparing thousands of runs, and mention integration with existing tools like TensorBoard or MLflow.

1. Clarify Requirements and Scale

Ask questions to understand expected scale (number of experiments, runs, metrics per run), user personas (researchers, engineers), and key features (tracking, visualization, comparison, collaboration).

2. Define Data Model and Storage

Design schemas for experiments, runs, parameters, metrics, and artifacts. Choose appropriate storage: relational DB for metadata, object store for artifacts, time-series DB for metrics, and discuss indexing for fast queries.

3. Design Core Services and APIs

Outline services for logging runs, querying experiments, and analyzing results. Define APIs for clients (Python SDK, UI) and consider asynchronous ingestion for high throughput.

4. Address Scalability and Performance

Discuss partitioning, caching, and distributed processing for large-scale metric aggregation. Consider trade-offs between consistency and availability, and how to handle hot spots.

5. Discuss Analysis and Visualization

Explain how to support interactive queries, comparative analysis, and visualization. Mention pre-aggregation, materialized views, and integration with notebooks or dashboards.

Key Points to Mention

  • Data model for experiments, runs, parameters, metrics, and artifacts with lineage tracking
  • Storage choices: relational DB for metadata, object store for artifacts, time-series or columnar DB for metrics
  • Scalable ingestion pipeline for high-volume metric logging (e.g., Kafka, batch writes)
  • Query and analysis capabilities: filtering, grouping, comparing runs, and statistical analysis
  • Trade-offs between consistency, latency, and cost in storage and query layers
  • Integration with existing ML tools and frameworks (e.g., TensorBoard, MLflow, Kubeflow)

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