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Start by clarifying requirements and scale, then walk through the full pipeline: data ingestion, indexing, retrieval, ranking, and serving. Emphasize ML-specific components like learning-to-rank and embedding-based retrieval, and discuss trade-offs between relevance, latency, and cost.
Pro tip: Anchor your design in Atlassian's context by referencing real product constraints (e.g., Jira's permission model, Confluence's rich content) and propose an iterative approach with offline evaluation and online A/B testing.
Ask about data volume, query types, latency SLAs, and relevance expectations. Establish scope (e.g., Jira issues vs. Confluence pages) and non-functional requirements like permissions and freshness.
Describe how to collect and preprocess data (e.g., issue text, comments, metadata) and build both inverted and vector indexes. Discuss incremental updates and handling permissions.
Explain candidate generation (e.g., BM25, ANN) and ranking (e.g., learning-to-rank with features like text match, recency, user behavior). Mention embedding models and fine-tuning.
Outline the serving layer: query understanding, parallel retrieval, ranking service, and result blending. Discuss caching, sharding, and latency optimization.
Cover offline metrics (NDCG, MRR), online A/B testing, and feedback loops. Highlight monitoring and continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through ingestion, tokenization, inverted index construction, and generating dense embeddings.
Start by clarifying requirements and constraints, then propose a scalable architecture using a distributed message queue and a sharded, versioned index. Emphasize how you handle updates and deletes efficiently with incremental indexing and compaction, and discuss trade-offs between consistency, latency, and cost.
Pro tip: Mention that deletes should be handled as tombstones with periodic compaction to avoid index bloat, and that using a Lambda architecture with a real-time and batch layer can balance freshness and throughput.
Ask about update frequency, latency SLAs, consistency requirements, and query patterns to tailor the design. This shows you understand that indexing pipelines are not one-size-fits-all.
Propose a distributed message queue (e.g., Kafka) to handle high-throughput updates and deletes, ensuring durability and decoupling. Use partitioning by document ID to maintain order per document.
Use a sharded, distributed search engine (e.g., Elasticsearch, Solr) or a custom inverted index with versioning. For updates, index new versions and mark old ones as stale; for deletes, insert tombstones.
Implement periodic compaction to merge segments, remove tombstones, and reclaim space. Ensure read consistency by using version numbers or timestamps to filter out stale documents.
Set up monitoring for lag, throughput, and index size; optimize by tuning batch sizes, parallelism, and compaction frequency. Discuss trade-offs between latency and resource usage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said sentence transformers as a starting point, then domain fine-tuning using click logs as weak supervision.
Start by clarifying the search context (e.g., Atlassian's Confluence/Jira content, query types, latency/scale constraints) and then recommend a baseline embedding model (e.g., a sentence-transformer or OpenAI embedding) with justification. For fine-tuning, propose a data generation pipeline using existing user interaction logs (click-through, query-document pairs) and synthetic query generation from documents, then outline a contrastive learning setup.
Pro tip: Emphasize that fine-tuning is only worth it if you have a clear evaluation metric and enough high-quality in-domain data; otherwise, a well-chosen off-the-shelf model with good chunking and hybrid search often outperforms a poorly fine-tuned one.
Ask about the data domain (e.g., technical docs, tickets), scale (millions of docs), latency needs, and whether multilingual or code search is required. This shows you tailor solutions to Atlassian's specific use case.
Suggest a strong general-purpose embedding model (e.g., E5, BGE, or OpenAI text-embedding-3) and justify based on performance, cost, and ease of deployment. Mention that you'd evaluate on a held-out set before considering fine-tuning.
Leverage existing user interaction logs (search queries, clicked documents, dwell time) and internal knowledge bases to create positive pairs. For negatives, use in-batch negatives or hard negatives mined from the corpus.
If logs are sparse, generate synthetic queries from documents using an LLM (e.g., 'generate a question this passage answers') and treat the source document as positive. Ensure diversity and filter for quality.
Fine-tune with a contrastive loss (e.g., MultipleNegativesRankingLoss) and evaluate using retrieval metrics (Recall@k, MRR, nDCG) on a human-annotated or synthetic test set. Discuss iterative improvement and A/B testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
NDCG and MRR for offline, A/B testing on click-through rate and session success metrics online.
Start by defining offline and online evaluation and their complementary roles in the ML lifecycle. Then outline a structured framework covering offline metrics, online experiments, and how to reconcile differences. Emphasize iterative improvement and guardrail metrics.
Pro tip: Highlight the importance of aligning offline metrics with online business metrics and using online experiments to validate offline gains, as offline improvements don't always translate to online success.
Clarify what you aim to evaluate: relevance, ranking quality, user engagement, or business impact. Align metrics with product objectives.
Use historical data and labeled datasets to compute metrics like NDCG, MAP, MRR, and precision/recall. Perform cross-validation and error analysis.
Run A/B tests or interleaving experiments to measure user behavior metrics (CTR, dwell time, conversion) and guardrail metrics (latency, failure rates).
Analyze discrepancies between offline and online results. Use online feedback to refine offline metrics and model. Iterate rapidly.
Continuously monitor online metrics post-launch, detect drift, and set up automated alerts for anomalies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Brought up user and project signals as features in a learning-to-rank layer.
Start by clarifying the problem scope and constraints, then propose a layered architecture that incorporates user-specific signals (roles, activity patterns) into the ranking pipeline. Emphasize trade-offs between personalization and other objectives like fairness, privacy, and system complexity, and suggest evaluation metrics to validate the approach.
Pro tip: Atlassian values collaboration and user-centric design, so highlight how personalization can improve team productivity without creating filter bubbles or privacy concerns. Mention the importance of explainability and user control over personalization settings.
Ask questions to understand the data available (user roles, activity logs), business goals (e.g., relevance, engagement), and constraints (latency, privacy, fairness).
Identify features from user roles and activity patterns, such as role-based permissions, recent actions, and collaboration networks, and how to encode them.
Propose a multi-stage ranking system where personalization is applied at different stages (e.g., candidate generation, ranking) using models like learning-to-rank with user embeddings.
Discuss trade-offs between personalization and diversity, fairness, privacy, and system complexity, and suggest techniques like regularization, differential privacy, or fallback to non-personalized results.
Define offline and online evaluation metrics (e.g., NDCG, CTR, user satisfaction) and propose A/B testing to measure impact and detect biases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.