An Interview with a FAANG engineer

Feedback about Atomic Parallelogram (the interviewee)

Advance this person to the next round?

Yes

Strengths: Structured Approach: The candidate laid out a clear and comprehensive outline at the beginning, addressing data, features, modeling, evaluation, and deployment. Feature Engineering Depth: Demonstrated good understanding of relevant features, including user behavior (reports, message velocity, interaction with known offenders), content-based features (text extraction from images, sinusoidal encoding for time), and considerations for various data types (text, image, audio, hyperlinks). Model Awareness: Showcased knowledge of various models (e.g., CLIP, ViT, audio models) and discussed their pros and cons. Correctly identified the potential for combined image and text to be offensive even if individual components are not. Handling Class Imbalance: Proactively identified class imbalance as a key challenge and suggested appropriate techniques (class weights, thresholds, Focal Loss) and evaluation metrics (AUC-PR, prevalence, segmented metrics). Business Objective Connection: Attempted to connect the solution to business objectives like revenue impact (reducing human review), advertiser safety, user trust, and legal considerations. Clarifying Questions: Asked relevant initial questions regarding scale, latency, data types, and categories of fraud. Deployment Considerations: Discussed different deployment strategies (shadow, canary, A/B testing) and monitoring aspects. Areas of Improvement: Initial Simplicity & Iteration: The proposed solution was quite complex from the outset. Consider starting with a simpler baseline model and then iterating towards complexity. Order of Discussion: It would be more logical to discuss model selection and its rationale before detailing very specific features. Critical Trade-offs: Did not explicitly ask about or address crucial trade-offs, such as the preference between precision and recall for scam detection in this specific context. Exploring Baselines/Alternatives: Could have discussed the potential of an existing/old model as a baseline or the concept of negative labeling. Nuance in Feature Design: Some feature ideas (e.g., "interaction with known offenders" as a binary feature) could benefit from more nuanced discussion, considering that many legitimate users might be contacted by scammers. Questioning Problem Constraints: Could have proactively questioned the necessity of a multi-task approach if one was implied, or explored simpler alternatives. Probing on Business Impact: While business objectives were mentioned, further probing on specific engagement metrics or deeper business impact could have strengthened the discussion. Attaching a template that I recommend for ML System Design 1. Clarification • Scope & Scale: Determine if the solution needs to handle real-time data processing or if it can be batch processed. Understand the scale in terms of data volume, velocity, and variety. • Business Objective/Key Metrics: Clearly define the business problem and the success metrics (e.g., accuracy, latency, throughput, user engagement). 2. High-Level ML Approach • Type of Learning: Decide between supervised, unsupervised (e.g., clustering), weakly supervised learning, or other approaches like reinforcement learning. • Trade-offs: Discuss the pros and cons of different approaches, considering factors like interpretability, complexity, and scalability. 3. Training Data • Data Collection & Logging: Understand the sources of data, frequency of logging, and the nature of data (structured, unstructured). • Data Imbalance: Address any class imbalance issues and the impact on the model's performance. 4. Feature Engineering • Feature Selection & Transformation: Discuss the process of selecting, creating, and transforming features to improve model performance. • Domain-Specific Features: Consider the inclusion of features that are particularly relevant to the business context. 5. Model Development & Training • Objective & Loss Function: Choose the appropriate loss function based on the problem (e.g., cross-entropy for classification, MSE for regression). • Hyperparameter Tuning: Consider factors like learning rate, batch size, model complexity, and regularization techniques. • Trade-offs: Balance between bias and variance, underfitting and overfitting, and address challenges like cold-start problems. • Model Bias: Consider potential biases in the model and their impact on fairness and ethics. 6. Evaluation • Metrics: Use appropriate online and offline metrics to evaluate the model's performance (e.g., precision, recall, AUC for classification, RMSE for regression). • Debugging & Holdout Sets: Ensure robust validation by using holdout sets and perform thorough debugging to catch potential issues. 7. Deployment • Real-Time vs. Batch Processing: Discuss the deployment strategy in terms of real-time predictions versus batch processing. • Monitoring & A/B Testing: Implement monitoring to detect issues in production, and use A/B testing to validate model improvements. • Continuous Learning: Consider the need for continuous learning to adapt to data drift and maintain model accuracy over time. Discuss potential problems if continuous learning is not implemented (e.g., model degradation due to concept drift).

Feedback about Nefarious Shadow (the interviewer)

Would you want to work with this person?

Yes