This is the kind of question where you can either go broad and shallow or try to go deep on one layer and run out of time on everything else.
Start by clarifying the product context and objectives, then walk through the ML pipeline stages: data collection, feature engineering, model training, evaluation, and serving. Emphasize trade-offs between relevance, diversity, and freshness, and how you would measure success with offline and online metrics.
Pro tip: Figma is a collaborative design tool, so highlight how the home feed could surface design files, comments, and team activity—focus on social and collaborative signals rather than just content consumption. Also, discuss how you'd handle cold-start for new users and new content in a design-centric environment.
Ask about the app's core user actions, business goals, and constraints. Define success metrics like CTR, dwell time, and user retention, and consider Figma-specific metrics like file opens or comment engagement.
Identify data sources: user interactions (views, likes, comments), content metadata (file type, recency, collaborators), and social graph. Design features like user embeddings, content embeddings, and real-time signals.
Choose a two-stage architecture: candidate generation (e.g., collaborative filtering, ANN) and ranking (e.g., deep learning with multi-task objectives). Discuss handling of cold-start and exploration vs exploitation.
Set up offline evaluation with metrics like recall@k, NDCG, and online A/B testing. Plan for continuous monitoring, feedback loops, and bias detection.
Design a scalable serving system with low-latency inference, caching, and fallback mechanisms. Discuss batch vs real-time feature serving, model versioning, and deployment strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through CTR, dwell time, and share probability as separate heads on a shared tower.
Start by defining the ranking problem in the context of Figma's search or recommendation system, then explain how to decompose it into multiple tasks such as relevance, engagement, and satisfaction. Describe the model architecture (e.g., shared bottom layers with task-specific heads) and the loss functions for each task, emphasizing how multi-task learning improves overall ranking by leveraging shared representations.
Pro tip: Highlight the importance of task weighting and potential negative transfer, and suggest using techniques like uncertainty weighting or GradNorm to balance tasks dynamically. Also, mention that offline metrics should align with online A/B test goals to ensure business impact.
Clarify the ranking scenario (e.g., search results, recommendations) and identify multiple objectives such as relevance, click-through rate, and user satisfaction. These become the tasks in the multi-task setup.
Propose a shared-bottom neural network with task-specific heads, or more advanced architectures like MMoE or PLE, to balance shared and task-specific learning.
For each task, define the target label (e.g., binary relevance, dwell time) and appropriate loss (e.g., cross-entropy, regression). Consider combining losses with learned weights.
Discuss techniques to handle task imbalance, negative transfer, and optimization, such as gradient normalization, uncertainty weighting, or task sampling.
Explain offline evaluation using per-task metrics and overall ranking metrics (e.g., NDCG), and how to validate via online A/B tests measuring business metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the product context and defining what 'freshness' and 'diversity' mean for Figma's design community. Then propose a multi-objective re-ranking framework that explicitly balances relevance with freshness and diversity using tunable weights or constraints, and describe how you would evaluate and iterate on the trade-offs.
Pro tip: Emphasize that you would treat freshness and diversity as soft constraints within a learning-to-rank model, rather than hard filters, to avoid relevance cliffs. Also mention the importance of offline metrics like nDCG and online A/B tests to quantify the impact.
Ask questions to understand what freshness and diversity mean for Figma (e.g., new design trends, diverse file types) and how relevance is measured. Identify business goals and user experience constraints.
Propose a re-ranking approach that combines relevance, freshness, and diversity scores, such as using a weighted sum or a constrained optimization. Explain how you would tune weights based on offline evaluation and online feedback.
Describe specific techniques: for diversity, use maximal marginal relevance (MMR) or submodular functions; for freshness, incorporate recency features or time-decay functions. Ensure these are integrated into the re-ranking model without hard filtering.
Outline an evaluation plan: offline metrics (nDCG, diversity metrics like intra-list similarity, freshness metrics like average age) and online A/B tests measuring engagement and satisfaction. Use these to adjust the balance.
Discuss how to monitor for relevance degradation and adapt weights dynamically based on context (e.g., user intent, query type). Mention fallback strategies if relevance drops below a threshold.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by outlining the end-to-end serving pipeline, emphasizing the stages and their latency budgets. Then discuss specific techniques to meet latency requirements at each stage, including trade-offs and optimizations. Finally, highlight how you monitor and iterate on latency performance.
Pro tip: Quantify latency budgets for each stage (e.g., retrieval <50ms, ranking <100ms, re-ranking <50ms) and explain how you enforce them. This shows you think in terms of concrete SLAs and can prioritize optimizations.
Give a high-level overview of the serving pipeline: retrieval (candidate generation), ranking (scoring), and re-ranking (final ordering). Mention the components, data flow, and typical request lifecycle.
State the overall latency SLA (e.g., <200ms) and break it down into per-stage budgets. Explain how you allocate time across retrieval, ranking, and re-ranking based on their computational complexity.
For each stage, detail specific optimizations: retrieval (ANN indexes, caching, sharding), ranking (model quantization, distillation, batching), re-ranking (cascade, early exit, lightweight models). Discuss trade-offs like accuracy vs. latency.
Describe how you measure latency (p50, p95, p99), set up alerts, and use A/B testing to balance latency and quality. Mention tools like Prometheus, Grafana, or custom dashboards.
Explain how the pipeline scales with traffic (horizontal scaling, load balancing) and handles failures (fallbacks, degradation) to maintain latency under load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.