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.
ML system evaluation and metrics
Design comprehensive evaluation strategies including offline metrics (precision, recall, F1, AUC, calibration), online metrics (A/B test setup, statistical significance), and business metrics. Understand metric limitations and how to avoid gaming metrics.
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.
Bias Identification and Mitigation
Recognizing and mitigating bias in experiments, data, models, and decision processes. Candidates should be able to identify common sources of bias such as selection bias, sampling bias, temporal effects, confounding variables, and feedback loops, and propose technical and experimental mitigations such as randomization, stratification, control groups, feature auditing, fairness metrics, and monitoring for drift. The topic also covers governance and process controls to reduce bias in measurement and product decisions.