The key thing nobody tells you before these rounds: they don't want breadth, they want you to pick a layer and go five levels deep.
Start by clarifying the problem scope and requirements, then walk through the system in stages: retrieval, ranking, and re-ranking. For each stage, explain the design choices, trade-offs, and how you would evaluate and iterate on the system.
Pro tip: Emphasize the importance of offline and online evaluation metrics, and how you would use A/B testing to validate improvements. Also, discuss how you would handle cold-start and scalability challenges, showing awareness of production constraints.
Ask questions to understand the scale, latency requirements, data available, and business objectives. Define success metrics such as NDCG, MRR, or business KPIs like conversion rate.
Explain how to efficiently fetch a candidate set from a large corpus using techniques like inverted indexes, dense retrieval with embeddings, or hybrid approaches. Discuss trade-offs between recall and latency.
Describe how to score and order candidates using machine learning models (e.g., LambdaMART, neural rankers). Cover feature engineering, model training, and handling of position bias.
Explain how to refine the top results using more expensive models or business rules, considering diversity, freshness, and personalization. Discuss trade-offs between relevance and other objectives.
Outline offline evaluation with historical data and online evaluation via A/B testing. Discuss how to monitor system health, detect degradation, and iterate on models and features.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that generation quality is multi-dimensional and depends on the task, then outline a layered evaluation strategy that combines automated metrics, human judgment, and online experimentation. Emphasize how you would choose metrics aligned with business objectives and validate them through A/B tests to ensure real-world impact.
Pro tip: Don't just list metrics—explain how you'd validate them against human judgments and business KPIs, and mention the importance of monitoring for distribution shifts and bias in production.
Identify what 'quality' means for the specific task (e.g., fidelity, coherence, relevance, diversity, safety) and align with business goals. Consider both intrinsic (output-only) and extrinsic (downstream task) evaluations.
Choose appropriate automatic metrics (e.g., BLEU, ROUGE, BERTScore, FID, IS for images) and learned evaluators (e.g., trained classifiers, LLM-as-a-judge) that correlate with human perception. Be transparent about their limitations.
Design human studies with clear rubrics (e.g., Likert scales, pairwise comparisons) and ensure annotator quality via training and agreement checks. Use human judgments as ground truth to calibrate automated metrics.
Conduct offline evaluations on held-out sets, then deploy A/B tests to measure impact on user engagement and business metrics. Use guardrail metrics to catch regressions in safety or fairness.
Continuously track quality metrics, detect drift, and collect user feedback to refine models and evaluation pipelines. Close the loop by feeding insights back into model development.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Frame the problem as a system design challenge: ensuring brand name correctness in generated images requires a multi-layered approach combining model conditioning, post-generation verification, and fallback mechanisms. Discuss trade-offs between generation quality, latency, and accuracy, and propose metrics to evaluate success.
Pro tip: Emphasize that perfect text generation is still an open problem; a robust system should include a verification step (e.g., OCR) and a fallback to overlay the brand name programmatically if the generated text is incorrect. This shows practical maturity and risk mitigation.
Ask about the use case: Is the brand name required to be perfectly legible? What is the acceptable latency? Are there multiple brand names or languages? This scopes the problem and informs trade-offs.
Use models that accept text conditioning (e.g., diffusion models with text encoders) and fine-tune on brand-specific data to improve fidelity. Consider incorporating a brand logo or font as a control signal.
Apply OCR or a dedicated text detection model to verify the brand name appears correctly. If not, trigger a fallback: regenerate, or overlay the brand name using a deterministic method.
For critical ads, route low-confidence outputs to human review or automatically composite the brand name. This ensures correctness at the cost of some automation.
Measure success via brand name accuracy (e.g., exact match rate), visual quality, and latency. Use A/B testing to compare approaches and continuously improve.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said SUTVA violation out loud and the interviewer visibly perked up, so that's worth knowing.
Start by acknowledging that network effects can cause interference between treatment and control groups, leading to biased estimates. Then propose a design that isolates the ranking change, such as cluster randomization or switchback experiments, and outline how to measure and mitigate contamination. Finally, discuss validation and sensitivity analyses to ensure robustness.
Pro tip: Emphasize that in two-sided markets like Amazon, network effects often spill over across units; using a cluster-based design with a sufficiently large number of clusters and pre-experiment covariate balance checks can make your results credible to stakeholders.
Map out how the ranking change could affect interactions between users, sellers, or products, and determine the likely pathways of contamination (e.g., shared inventory, social influence).
Select a unit of randomization (e.g., geographic region, seller cluster, time-based switchback) that aligns with the network structure to reduce spillover between treatment and control.
Calculate sample size accounting for intra-cluster correlation, and set a duration that captures the ranking change's effect while avoiding novelty or primacy effects.
Use techniques like difference-in-differences, instrumental variables, or exposure modeling to quantify and correct for any residual interference.
Conduct placebo tests, vary cluster definitions, and compare with alternative designs (e.g., switchback) to ensure conclusions are not driven by contamination.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer: you have to mention it or they'll bring it up themselves.
Start by defining position bias and its impact on implicit feedback, then outline a multi-pronged strategy that combines model-based correction (e.g., inverse propensity scoring) with unbiased evaluation. Emphasize the trade-offs between different debiasing techniques and how you would validate their effectiveness in an online setting.
Pro tip: Highlight that position bias is not just a modeling problem but also a data collection issue; propose logging randomized exploration data or using counterfactual evaluation to ground your solution in real-world constraints.
Explain what position bias is in ranking, why implicit feedback (clicks, purchases) is biased by position, and how it can degrade ranking quality if ignored.
Discuss methods such as inverse propensity scoring (IPS), click models, or unbiased learning-to-rank, and justify your choice based on data availability and business constraints.
Describe how you would implement the chosen method in the training pipeline, including propensity estimation, loss function modification, and handling of sparse data.
Outline offline evaluation using counterfactual or unbiased metrics, and online A/B testing to measure real-world impact, with a plan for continuous monitoring and refinement.
Acknowledge potential drawbacks such as increased variance, computational cost, or reliance on propensity model accuracy, and propose mitigation strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.