The no-ML constraint was the part that threw me off initially.
Start by clarifying requirements and scale, then walk through the pipeline stages: ingestion (crawling, parsing, deduplication), indexing (inverted index, tokenization, stemming), ranking (non-ML heuristics like BM25, recency, source authority, engagement), and serving (query processing, caching, sharding). Emphasize trade-offs and justify design choices without ML, focusing on deterministic, explainable ranking signals.
Pro tip: Highlight that without ML, you can still use learning-to-rank with hand-tuned weights or simple linear models, but the key is to rely on robust information retrieval fundamentals like BM25 and careful feature engineering. Also, mention the importance of A/B testing and offline evaluation to iteratively improve ranking.
Ask about expected query volume, document corpus size, latency requirements, and freshness needs. This shapes decisions on indexing and serving architecture.
Describe how to fetch articles from publishers, parse and normalize content, extract metadata (title, body, author, publish time), and deduplicate. Consider incremental updates and handling of paywalled content.
Explain creating an inverted index with tokenization, stemming, and stop-word removal. Discuss storing additional fields for ranking (e.g., recency, source authority) and supporting phrase queries.
Propose a non-ML ranking using BM25 for text relevance, combined with heuristics like recency boost, source authority, and user engagement signals. Describe how to tune weights via offline evaluation.
Outline query processing, retrieval from index shards, merging results, and applying ranking. Discuss caching, load balancing, and latency optimization.
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Went with a pub/sub model where article publishers push events, a processing layer handles tokenization and index updates, and writes go to a distributed index.
Start by clarifying requirements (freshness SLA, scale, consistency) and then outline a streaming ingestion pipeline that processes articles in near real-time, followed by an indexing layer optimized for low-latency search. Emphasize trade-offs between freshness, cost, and consistency, and how ML components (e.g., embeddings, ranking) integrate into the pipeline.
Pro tip: Highlight the importance of decoupling ingestion from indexing using a message queue (e.g., Kafka) to handle bursts and ensure fault tolerance, and mention how you'd monitor end-to-end latency with metrics like p99 indexing time.
Ask about expected article volume, freshness SLA (e.g., <5 minutes), search latency, consistency needs, and budget. This shows you understand the problem before designing.
Propose a scalable ingestion layer using a distributed message queue (e.g., Kafka) to buffer incoming articles, with consumers that parse, enrich, and transform data. Include error handling and dead-letter queues.
Describe near real-time processing (e.g., stream processing with Flink/Spark Streaming) to extract metadata, compute ML features (e.g., embeddings, categories), and validate content. Ensure idempotency and exactly-once semantics if needed.
Explain how to index documents into a search engine (e.g., Elasticsearch, Vespa) with low-latency updates. Discuss sharding, replication, and near real-time indexing capabilities (e.g., Elasticsearch refresh interval).
Outline monitoring for end-to-end latency, throughput, and error rates. Discuss strategies for backpressure, retries, and ensuring data consistency between ingestion and indexing.
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BM25 was the obvious starting point and I explained it reasonably well.
Start by clarifying the search context (e.g., document search, product search) and the available data. Then, outline a classic learning-to-rank approach using hand-crafted features and a simple scoring function, emphasizing that even without ML, you can leverage heuristics and statistical signals. Finally, discuss how to combine signals via a weighted sum or rank fusion, and mention evaluation metrics.
Pro tip: Emphasize that you would start with a simple, interpretable baseline (e.g., TF-IDF + PageRank) and iterate based on user feedback, rather than overcomplicating from the start. This shows pragmatism and product sense.
Ask about the type of search (web, e-commerce, enterprise), data available (click logs, document metadata), and latency requirements. This ensures your solution is tailored.
List signals such as textual relevance (TF-IDF, BM25), document quality (PageRank, freshness), user behavior (click-through rate, dwell time), and personalization (user history). Explain how to compute them without ML.
Propose a linear combination with weights (e.g., score = w1*BM25 + w2*PageRank + w3*CTR). Discuss how to set weights via heuristics, A/B testing, or simple optimization.
Explain that signals have different scales, so normalize them (e.g., min-max, z-score) before combining. Mention rank fusion methods like Reciprocal Rank Fusion as an alternative.
Describe offline metrics (NDCG, MAP) and online metrics (CTR, conversion). Suggest starting with a simple baseline and iterating based on user feedback.
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Talked about offline evaluation using human-labeled relevance judgments and NDCG, plus online A/B testing on click metrics.
Start by defining clear offline and online metrics for search quality, then describe how to use heuristics and manual rules to build a baseline ranking function. Explain how to iterate using A/B testing and qualitative feedback, and finally discuss how to decide when an ML model becomes necessary.
Pro tip: Emphasize that without an ML model, you rely on domain expertise and rapid experimentation; but also highlight that you would instrument everything to collect data for future ML models, showing foresight.
Identify both offline metrics (e.g., NDCG, MAP) and online metrics (e.g., CTR, dwell time, task success) that align with user needs and business goals.
Create a simple ranking function using hand-tuned rules or weighted features (e.g., text match, popularity, freshness) to establish a baseline for comparison.
Run controlled experiments to test changes to the ranking function, measuring impact on online metrics and ensuring statistical significance.
Use user studies, click models, and manual relevance judgments to identify weaknesses and guide improvements beyond quantitative metrics.
Monitor diminishing returns from manual tuning and assess whether enough labeled data exists to justify building an ML model for further gains.
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This was the part I actually felt good about.
Start by clarifying the system's current requirements and data flow, then propose a modular architecture with clear interfaces that can later accommodate an ML ranker. Emphasize designing for data collection, feature logging, and a fallback ranking mechanism from day one.
Pro tip: Show that you understand the importance of logging features and outcomes now to enable future ML training, and mention that you would design the system to A/B test the ML ranker against the heuristic to measure impact.
Ask questions to understand the current system, scale, latency requirements, and what 'enough data' means. Identify the key metrics for success.
Propose a layered architecture with a ranking service that abstracts the ranking logic. Define clear interfaces so the ranking algorithm can be swapped without affecting other components.
Ensure the system logs all necessary data for training an ML model, including user interactions, item features, and context. Use a feature store to manage features consistently.
Design the system to support A/B testing and gradual rollout of the ML ranker. Include monitoring and fallback mechanisms to handle failures.
Discuss how to handle increased load and model updates, and how to continuously improve the ML model with new data.
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Sharding by document ID hash for even distribution, replicas for read throughput, and a caching layer in front for popular queries.
Start by clarifying requirements: index size, QPS, latency, consistency, and update patterns. Then propose a sharding strategy (e.g., by document ID or term) and replication for fault tolerance and read scalability, discussing trade-offs like consistency vs. availability and hot shard mitigation. Finally, explain how to handle rebalancing, failover, and monitoring at global scale.
Pro tip: Emphasize that sharding and replication choices must align with the specific ML workload (e.g., embedding search vs. keyword search) and Apple's privacy constraints; mention that you'd prototype with real traffic patterns to validate assumptions before full rollout.
Ask about index size, QPS, latency SLA, consistency needs, update frequency, and geographic distribution. Also consider privacy and data residency requirements.
Evaluate sharding by document ID (hash-based) for even distribution vs. by term or semantic cluster for query efficiency. Discuss trade-offs like hot shards and cross-shard queries.
Propose primary-replica or multi-primary replication per shard, with synchronous vs. asynchronous replication trade-offs. Consider quorum-based consistency for high availability.
Use geo-distributed clusters with a routing layer that directs queries to the nearest replica. Discuss data sovereignty and cross-region replication lag.
Describe automated shard rebalancing, health checks, and failover procedures. Include metrics for shard load, replication lag, and query latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.