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.
Generative Artificial Intelligence for Enterprise
Focuses on practical use of artificial intelligence and generative artificial intelligence within enterprise information technology and business processes. Candidates should explain foundational model concepts, common generative use cases such as assisted content generation knowledge retrieval conversational interfaces intelligent automation and code assistance, and how these models augment decision workflows. Interviewers evaluate knowledge of data and model governance, model training and evaluation, productionization and model operations, monitoring and detection of model drift, privacy and security controls, explainability and fairness, human in the loop design and prompt engineering, compute and cost trade offs, deployment options and integration with existing applications and data platforms, and legal and ethical considerations. Candidates should be able to assess feasibility of use cases, design safe and compliant deployment approaches, measure business value and risk, and integrate model governance with enterprise data protection and compliance programs.
Autonomous Vehicle Technology and Strategy
Knowledge of autonomous vehicle technology, operational requirements, and strategic integration with a mobility platform. Topics include sensor technologies and sensor fusion, localization and mapping techniques, perception and planning pipelines, simulation and validation strategies, edge and fleet compute and networking requirements, high volume telemetry and data pipeline design, safety engineering and validation practices, regulatory and policy considerations, and partnership models with vehicle manufacturers and autonomy providers. Candidates should be able to discuss how platform architecture, data infrastructure, and operational processes must change to support autonomous vehicle deployment including low latency requirements, over the air updates, fleet coordination, and integration with rider and driver systems.