Machine Learning & AI Topics
Production machine learning systems, model development, deployment, and operationalization. Covers ML architecture, model training and serving infrastructure, ML platform design, responsible AI practices, and integration of ML capabilities into products. Excludes research-focused ML innovations and academic contributions (see Research & Academic Leadership for publication and research contributions). Emphasizes applied ML engineering at scale and operational considerations for ML systems in production.
Debugging and Troubleshooting AI Systems
Covers systematic approaches to find and fix failures in machine learning and artificial intelligence systems. Topics include common failure modes such as poor data quality, incorrect preprocessing, label errors, data leakage, training instability, vanishing or exploding gradients, numerical precision issues, overfitting and underfitting, optimizer and hyperparameter problems, model capacity mismatch, implementation bugs, hardware and memory failures, and production environment issues. Skills and techniques include data validation and exploratory data analysis, unit tests and reproducible experiments, sanity checks and simplified models, gradient checks and plotting training dynamics, visualizing predictions and errors, ablation studies and feature importance analysis, logging and instrumentation, profiling for latency and memory, isolating components with canary or shadow deployments, rollback and mitigation strategies, monitoring for concept drift, and applying root cause analysis until the underlying cause is found. Interviewers assess the candidate on their debugging process, ability to isolate issues, use of tools and metrics for diagnosis, trade offs in fixes, and how they prevent similar failures in future iterations.
Model Monitoring and Observability
Covers the design, implementation, operation, and continuous improvement of monitoring, observability, logging, alerting, and debugging for machine learning models and their data pipelines in production. Candidates should be able to design instrumentation and telemetry that captures predictions, input features, request context, timestamps, and ground truth when available; define and track online and offline metrics including model quality metrics, calibration and fairness metrics, prediction latency, throughput, error rates, and business key performance indicators; and implement logging strategies for debugging, auditing, and backtesting while addressing privacy and data retention tradeoffs. The topic includes detection and diagnosis of distribution shifts and concept drift such as data drift, label drift, and feature drift using statistical tests and population comparison measures (for example Kolmogorov Smirnov test, population stability index, and Kullback Leibler divergence), windowed and embedding based comparisons, change point detection, and anomaly detection approaches. It covers setting thresholds and service level objectives, designing alerting rules and escalation policies, creating runbooks and incident response processes, and avoiding alert fatigue. Candidates should understand retraining strategies and triggers including scheduled retraining, automated retraining based on monitored signals, human in the loop review, canary and phased rollouts, shadow deployments, A versus B experiments, fallback logic, rollback procedures, and safe deployment patterns. Also included are model artifact and data versioning, data and feature lineage, reproducibility and metadata capture for auditability, continuous validation versus scheduled validation tradeoffs, pipeline automation and orchestration for retraining and deployment, and techniques for root cause analysis and production debugging such as sample replay, feature distribution analysis, correlation with upstream pipeline metrics, and failed prediction forensics. Senior expectations include designing scalable telemetry pipelines, sampling and aggregation strategies to control cost while preserving signal fidelity, governance and compliance considerations, cross functional incident management and postmortem practices, and trade offs between detection sensitivity and operational burden.
AI and Machine Learning Background
A synopsis of applied artificial intelligence and machine learning experience including models, frameworks, and pipelines used, datasets and scale, production deployment experience, evaluation metrics, and measurable business outcomes. Candidates should describe specific projects, roles played, research versus production distinctions, and technical choices and trade offs.