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.
AI and Automation in Recruiting
This topic tests familiarity with how artificial intelligence and automation are applied to recruiting, along with practical implementation and governance considerations. Candidates should describe common use cases such as resume parsing and ranking, candidate matching and rediscovery, intelligent outreach and scheduling, chatbots for candidate engagement, and automated assessment workflows; how to measure impact using conversion and quality metrics; trade offs between automation and human review; and ethical and legal risks such as bias, transparency, and data privacy. Discuss integration with applicant tracking systems, controlled experiments to evaluate impact, and how to establish monitoring and guardrails before production deployments.
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.