This is the core question and it's deceptively wide.
Start by clarifying requirements and scale, then propose a modular multimodal pipeline with separate but fused models for image, text, and video. Emphasize trade-offs between latency, accuracy, and cost, and discuss how to handle policy evolution and adversarial attacks.
Pro tip: Highlight the importance of a human-in-the-loop review system and active learning to continuously improve the model, especially for edge cases and new policy violations.
Ask about scale (e.g., ads per day), latency requirements, policy definitions, and acceptable precision/recall trade-offs. Understand what modalities are most critical and how violations are defined.
Propose separate encoders for each modality (e.g., CNN for images, transformer for text, 3D CNN or video transformer for video) and a fusion mechanism (early, late, or hybrid). Consider using pre-trained models and fine-tuning.
Discuss data collection (labeled violations, synthetic data), class imbalance, and multi-label classification. Mention techniques like data augmentation, hard negative mining, and transfer learning.
Cover model serving (batch vs. real-time), latency-accuracy trade-offs (model distillation, quantization), and scalability (distributed inference). Include monitoring for drift and adversarial attacks.
Explain how to integrate human moderators for ambiguous cases and use their feedback for active learning. Discuss metrics like precision@k and how to handle policy updates.
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Trickier than it sounds because policy definitions shift and annotators disagree on edge cases.
Start by clarifying the policy violation definition and the scale of the problem, then outline a multi-stage labeling pipeline that combines automated heuristics, human review, and active learning to efficiently build a high-quality ground truth dataset. Emphasize trade-offs between label quality, cost, and coverage, and propose metrics to validate the ground truth.
Pro tip: Propose a tiered labeling strategy where ambiguous cases are escalated to specialized reviewers, and use inter-annotator agreement metrics to continuously monitor label quality. Also, suggest creating a small, high-confidence 'golden set' to calibrate models and detect label drift over time.
Work with policy experts to create a clear, exhaustive taxonomy of violation types and sub-types, along with detailed annotation guidelines and edge-case examples.
Determine sampling strategy (random, stratified, uncertainty-based) and set up a labeling workflow with multiple annotators, quality checks, and adjudication for disagreements.
Use heuristics, existing models, and user reports to generate noisy labels, then apply active learning to prioritize uncertain or diverse samples for human labeling, reducing cost.
Measure inter-annotator agreement, audit labels, and compare against a golden set; iterate on guidelines and pipeline based on error analysis and model performance.
Set up continuous labeling for new data, monitor label drift, and establish feedback loops between model predictions and human review to maintain ground truth quality.
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I went with a late-fusion approach and explained the tradeoffs versus early fusion.
Start by clarifying the product goal and data constraints, then propose a modular architecture with modality-specific encoders and a fusion layer. Walk through the design choices for each component, emphasizing how you would handle missing modalities and scale training. Conclude with evaluation metrics and iteration strategy.
Pro tip: Meta values production-ready designs, so explicitly discuss how you'd handle missing modalities at inference and how you'd monitor fusion quality in production. Also, mention that you'd start with a simple late fusion baseline before moving to complex cross-attention, showing pragmatism.
Ask about the product use case, available modalities, data volume, latency, and compute budget. This ensures your design is grounded in real constraints.
Outline modality-specific encoders (e.g., CNN for images, transformer for text) and a fusion mechanism. Explain why you chose early, late, or hybrid fusion based on the task.
Describe how signals are combined: concatenation, attention, or gating. Discuss handling missing modalities via masking or modality dropout during training.
Explain how you'd train the model (e.g., multi-task learning, pre-training) and scale it (distributed training, mixed precision). Mention techniques to avoid overfitting.
Specify offline metrics (e.g., accuracy, F1, AUC) and online metrics (e.g., CTR, engagement). Describe how you'd monitor fusion quality and iterate.
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Went through model distillation, async processing for video, and caching embeddings for near-duplicates.
Start by clarifying the problem scope: define latency SLA (e.g., p99 < 100ms), throughput (QPS), and model complexity. Then propose a tiered serving architecture that balances latency and throughput, using techniques like model distillation, caching, and hardware acceleration. Finally, discuss trade-offs and monitoring to ensure reliability at scale.
Pro tip: Emphasize the importance of end-to-end latency budgeting and how you would instrument each stage to identify bottlenecks. Show awareness that in ads, a slight latency increase can directly impact revenue, so you must optimize for both latency and throughput without sacrificing model quality.
Ask about expected QPS, latency SLA (p50/p99), model size, and hardware availability. Understand the business impact of latency vs. throughput.
Propose a multi-tier system: a fast, lightweight model for most requests and a heavier model for a subset. Use caching for frequent queries and precomputation where possible.
Apply model compression (quantization, pruning, distillation), use optimized runtimes (TensorRT, ONNX), and batch requests dynamically to improve throughput without violating latency.
Utilize GPUs/TPUs with autoscaling, load balancing, and request prioritization. Consider edge caching and CDN for static features.
Set up real-time monitoring for latency, throughput, and error rates. Implement A/B testing to measure impact on business metrics and continuously refine.
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Start by framing the threshold decision as a business problem, not just a modeling problem. Explain how you would quantify the costs of false positives and false negatives, then use that to choose an operating point that optimizes the relevant metric. Emphasize experimentation and iteration to validate the choice in production.
Pro tip: Mention that you would align the threshold with the product's key performance indicator (e.g., user reports or engagement) and run A/B tests to measure the causal impact of threshold changes. This shows you think beyond offline metrics and consider real-world user behavior.
Clarify what the moderation system aims to achieve: minimize harmful content exposure while avoiding over-removal of benign content. Identify key metrics like prevalence of harm, user reports, and removal rates.
Estimate the relative cost of a false positive (e.g., user frustration, appeals) versus a false negative (e.g., user harm, trust erosion). This can be done via user surveys, historical data, or stakeholder input.
Use the cost ratio to choose a threshold that minimizes total expected cost, or maximizes a metric like F-beta that weights precision and recall according to business priorities.
Evaluate the chosen threshold on a holdout set, then run A/B tests to measure impact on user behavior and business metrics. Be prepared to adjust based on results.
Continuously monitor performance and re-evaluate thresholds as data distributions and business priorities shift. Set up alerts for drift and periodic reviews.
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Shorter discussion but they wanted to know how you prioritize what reviewers see first.
Start by clarifying the classifier's role, error modes, and business objectives to ground the design. Then outline the human review queue as a socio-technical system, covering prioritization, reviewer workflow, feedback loops, and metrics. Conclude with trade-offs and a phased rollout plan.
Pro tip: Emphasize that the queue's primary goal is to maximize learning per review, not just correct errors—this shows you understand ML system design beyond surface-level queueing.
Ask about classifier accuracy, latency, volume, review capacity, and business impact to scope the design. Identify what types of errors are most costly and how feedback will be used.
Design a scoring function to rank items by uncertainty, impact, and diversity. Consider active learning principles to select the most informative samples for review.
Specify how reviewers interact with the queue: task assignment, labeling UI, quality control, and escalation paths. Ensure efficiency and minimize cognitive load.
Define how reviewed labels feed back into model retraining and how to measure queue effectiveness (e.g., model improvement per review, reviewer agreement).
Address infrastructure needs, throughput, latency, and monitoring for drift or bottlenecks. Discuss phased rollout and A/B testing.
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This was the part I found most interesting and also where I ran out of time.
Frame the problem as an adversarial robustness challenge and propose a multi-layered defense strategy. Start by acknowledging that no single filter is perfect, then outline a system that combines proactive detection, adaptive learning, and human-in-the-loop review to stay ahead of evaders.
Pro tip: Emphasize the importance of continuously updating models with adversarial examples and monitoring for distribution shifts, as evasion tactics evolve. Mention that robustness is an ongoing process, not a one-time fix.
Enumerate common evasion techniques such as image obfuscation (e.g., noise, filters, adversarial patches) and coded language (e.g., leetspeak, euphemisms, context-dependent slang).
Propose combining image and text analysis with contextual signals (e.g., user behavior, metadata) to catch obfuscation that single-modality models might miss.
Suggest training models on adversarially perturbed examples and using techniques like data augmentation and robust optimization to improve resilience.
Describe a system that continuously collects new evasion attempts, labels them (with human review), and retrains models to adapt to emerging tactics.
Outline metrics for detection performance (e.g., precision/recall on adversarial sets) and set up alerts for sudden drops, enabling rapid response.
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