Databricks·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

Senior

Jun 2026

Summary

Databricks ML Engineer interview focused entirely on designing a content moderation system for LLM outputs. One big open-ended system design question that touched everything from modeling choices to serving infrastructure to eval. Pretty intense scope for a single session.

Questions Asked (1)

Design an ML system that detects harmful content in LLM-generated outputs, covering categories like hate speech, violence, sexual content, self-harm, and prompt injection. Walk through your full approach from requirements to serving to evaluation.

System DesignTechnical Trade-offsProduct Analytics & Metrics

Author's notes

This question has a lot of surface area and I think I underestimated how much they wanted me to go deep on the modeling tradeoffs vs.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a multi-layered architecture that combines fast rule-based filters, ML classifiers, and LLM-based judges for nuanced cases. Emphasize trade-offs between latency, cost, and accuracy, and describe how you would evaluate and monitor the system in production.

Pro tip: Frame the system as a defense-in-depth pipeline where each layer catches what the previous misses, and explicitly discuss how you'd handle adversarial prompt injection as a separate, evolving threat model.

1. Clarify Requirements and Constraints

Ask about latency budgets, throughput, acceptable false positive/negative rates, content categories, and whether detection is pre- or post-generation. This shapes the entire design.

2. Design Multi-Layer Detection Pipeline

Propose a cascade: fast regex/keyword filters, lightweight ML classifiers (e.g., fine-tuned BERT) for each category, and an LLM-as-judge for ambiguous cases. Include prompt injection detection via input sanitization and output anomaly detection.

3. Address Data and Model Training

Discuss data sourcing (human-labeled, synthetic, adversarial), handling class imbalance, and techniques like active learning and continual learning to adapt to new harmful patterns.

4. Plan Serving and Scalability

Describe deployment on Databricks (e.g., MLflow, Model Serving), caching, batching, and fallback strategies. Consider cost and latency trade-offs for each layer.

5. Define Evaluation and Monitoring

Outline offline metrics (precision/recall per category, AUC), online A/B testing, and production monitoring for drift, adversarial attacks, and human-in-the-loop feedback.

Key Points to Mention

Defense-in-depth with multiple layers (rules, ML, LLM) to balance latency and accuracy
Handling prompt injection as a distinct adversarial threat requiring input/output validation
Trade-offs between false positives (over-blocking) and false negatives (harmful content leakage)
Use of LLM-as-a-judge for nuanced cases but with cost and latency considerations
Continuous evaluation and adaptation using human feedback and adversarial testing
Leveraging Databricks ecosystem (MLflow, Delta Lake, Model Serving) for MLOps

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.