Capital One·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

Senior

Jun 2026

Summary

Capital One ML Engineer system design round, focused entirely on building a rule engine for a virtual credit card validation service. Pretty interesting problem space but the scope kept expanding as the interview went on.

Questions Asked (1)

Design a rule engine API for validating virtual credit card transactions. The API should accept a transaction and a list of rules, and return whether the transaction is valid along with any rules that were violated.

System DesignAPI & IntegrationsTechnical Trade-offs

Author's notes

This one sprawled fast.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scope, then outline a high-level architecture that separates rule definition, evaluation, and result aggregation. Focus on extensibility, performance, and how ML models can be integrated as rules. Conclude with trade-offs and potential optimizations.

Pro tip: Emphasize that rules should be data-driven and versioned, and that the engine should support both deterministic and ML-based rules, which is crucial for a company like Capital One that leverages machine learning for fraud detection.

1. Clarify Requirements and Scope

Ask questions to understand expected throughput, latency, rule complexity, and whether rules are static or dynamic. Clarify if ML models are considered rules and how they should be integrated.

2. Define API Contract

Design the request and response schemas: transaction object, list of rules, and response with validity and violated rules. Consider using JSON for flexibility and include metadata like rule IDs and messages.

3. Design Rule Engine Architecture

Propose a modular architecture with a rule parser, evaluator, and aggregator. Discuss how to handle different rule types (e.g., threshold, pattern, ML model) and ensure scalability and fault tolerance.

4. Address Performance and Scalability

Discuss strategies like parallel rule evaluation, caching, and asynchronous processing. Mention how to handle high-volume transactions and potential bottlenecks.

5. Discuss Trade-offs and Extensions

Talk about trade-offs between simplicity and flexibility, and how to extend the system for new rule types. Mention monitoring, logging, and versioning of rules.

Key Points to Mention

Rule representation: DSL, JSON, or code-based rules, and how to version them.
Integration of ML models as rules, including model serving and feature computation.
Evaluation strategies: sequential vs. parallel, short-circuiting, and conflict resolution.
API design best practices: idempotency, error handling, and rate limiting.
Scalability considerations: horizontal scaling, caching, and message queues.
Security and compliance: PII handling, audit trails, and regulatory requirements.

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