This is the kind of question that feels manageable until you're 10 minutes in and realize you've been talking about tokenization for way too long.
Start by clarifying requirements and constraints, then walk through the three stages—query understanding, retrieval, and ranking—highlighting data sources, model choices, and trade-offs. Emphasize how personalization and business metrics (e.g., conversion, delivery time) influence each stage.
Pro tip: Anchor your design around DoorDash's unique data (order history, real-time availability, delivery logistics) and discuss how you'd handle cold-start users and the exploration-exploitation trade-off in ranking.
Ask about scale, latency, personalization signals, and business objectives (e.g., maximizing orders vs. delivery efficiency). Define success metrics like CTR, conversion, or user satisfaction.
Parse the user query to extract intent, entities (cuisine, location, price), and implicit preferences from user history. Use NLP techniques like NER, intent classification, and query rewriting.
Generate candidate restaurants using multiple sources: keyword search, collaborative filtering, and content-based matching. Apply filters (open now, delivery zone) and ensure low latency with approximate nearest neighbor search.
Score and order candidates using a learning-to-rank model that combines features from query, user, restaurant, and context. Consider business rules and diversity to avoid filter bubbles.
Offline evaluate with metrics like NDCG, then online A/B test. Monitor for biases and feedback loops, and iterate on model and features.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about things like current session behavior, time of day, and whether a restaurant is trending in the area.
Start by identifying high-value real-time signals for DoorDash's ranking model, such as courier location, restaurant prep time, and current traffic. Then, discuss a low-latency serving architecture that balances freshness, cost, and reliability, using a feature store with online-offline consistency and caching strategies.
Pro tip: Emphasize the trade-off between signal freshness and system complexity: not all signals need millisecond freshness; some can be updated every few seconds or minutes. Also, mention the importance of monitoring and fallback mechanisms to handle stale or missing features gracefully.
List relevant real-time signals for DoorDash, such as courier GPS, restaurant order queue length, traffic conditions, and weather. Prioritize signals by their impact on ranking and feasibility of low-latency serving.
Propose a streaming pipeline (e.g., Kafka + Flink) to compute features in real-time, and store them in a low-latency feature store (e.g., Redis, DynamoDB) for online serving. Ensure offline-online consistency for training.
Discuss techniques like in-memory caching, pre-computation, and approximate algorithms to meet latency SLAs. Consider edge computing for location-based signals.
Implement fallback to batch features or default values when real-time signals are unavailable. Use time-to-live (TTL) and freshness checks to avoid stale data.
Set up A/B tests to measure the impact of real-time signals on business metrics. Monitor latency, feature freshness, and model performance in production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with content-based fallbacks for new restaurants (menu embeddings, cuisine tags, location) and popularity-based defaults for new users.
Start by clarifying the two cold-start scenarios (new restaurants and new users) and their distinct challenges. Then propose a multi-pronged strategy that leverages available metadata, content-based methods, and exploration, while emphasizing the need for a feedback loop to quickly learn from early interactions. Conclude with how you would measure success and iterate.
Pro tip: Highlight the importance of using transfer learning from similar entities (e.g., restaurants in the same cuisine or users with similar demographics) and designing an exploration-exploitation trade-off that balances short-term performance with long-term data collection.
Ask clarifying questions to understand the specific cold-start scenario: is it a new restaurant with no orders, a new user with no history, or both? What data is available (e.g., restaurant attributes, user sign-up info)? What are the business metrics (e.g., conversion, order rate)?
For new restaurants, use attributes like cuisine, price range, location, and menu items to match with user preferences. For new users, use sign-up information (e.g., location, device, referral source) and demographic data to infer preferences.
Use models trained on similar entities: for restaurants, find similar existing restaurants and use their embeddings; for users, find similar users (e.g., via collaborative filtering or clustering) and borrow their preferences.
Implement a bandit-based approach (e.g., Thompson sampling) to balance showing new items to gather data while minimizing poor user experiences. Consider contextual bandits to personalize exploration.
Define metrics to measure cold-start performance (e.g., time to first order, user retention, restaurant order volume). Set up A/B tests to compare strategies and continuously update models as data accumulates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the follow-up I was least prepared for.
Start by clarifying the recommendation system's current architecture and business goals, then propose LLM integration points that address specific pain points, such as query understanding, explanation generation, or ranking. Emphasize trade-offs between latency, cost, and quality, and suggest a phased approach with offline evaluation before online deployment.
Pro tip: Focus on measurable impact: propose A/B tests with metrics like CTR, conversion, and order value, and discuss how to handle LLM hallucinations in explanations or ranking. Showing awareness of production constraints like latency and cost will set you apart.
Ask about the current system's scale, latency requirements, and business objectives to tailor your LLM integration. Identify where LLMs can add the most value without disrupting existing pipelines.
Map LLM capabilities to specific components: query reformulation for search, explanation generation for trust, end-to-end ranking for personalization, or RAG for grounding in reviews and menus. Prioritize based on impact and feasibility.
Outline how the LLM will be used: prompt engineering, fine-tuning, retrieval augmentation, and fallback mechanisms. Consider model size, inference latency, and cost, and propose caching or distillation if needed.
Define offline metrics (e.g., NDCG, BLEU for explanations) and online A/B tests (CTR, conversion). Plan for monitoring and continuous improvement, including guardrails against hallucinations.
Discuss trade-offs between latency and quality, cost and performance, and personalization and privacy. Propose mitigations like asynchronous processing or hybrid models.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.