I went straight to the search indexing layer and spent probably too long on it.
Start by clarifying the scope and requirements with the interviewer, focusing on core features like job posting, search, and application. Then, design a scalable architecture that handles high read traffic and complex search queries, discussing trade-offs between consistency, availability, and latency. Finally, dive into key components like search indexing, ranking algorithms, and data storage.
Pro tip: Emphasize the importance of search relevance and ranking, as it's the core value proposition for users. Discuss how you would measure and improve relevance using A/B testing and user feedback.
Ask questions to understand functional and non-functional requirements, such as scale, latency, and consistency needs. Define core features: job posting, search, application, and user profiles.
Sketch the main components: web servers, application servers, databases, search index, and caching layers. Discuss how data flows from job posting to search and application.
Explain how to implement search using an inverted index (e.g., Elasticsearch). Discuss tokenization, stemming, and ranking based on relevance signals like recency, location, and user behavior.
Address scaling reads and writes: sharding, replication, caching, and CDN. Discuss trade-offs between consistency and availability (CAP theorem) and how to handle peak loads.
Talk about how to measure success: click-through rate, application rate, and time-to-hire. Suggest features like personalized recommendations and alerts to improve user engagement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.