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.
Artificial Intelligence and Machine Learning Progression
Personal career narrative focused on progression within artificial intelligence and machine learning domains toward senior or staff level roles. Candidates should highlight domain specific milestones such as research contributions, production AI systems designed or architected, scale and complexity of models and pipelines, leadership of ML initiatives, cross functional influence on product or infrastructure, publications or patents if applicable, and how technical depth and organizational impact grew over time. Include concrete examples of projects, measures of system performance or business impact, and how domain expertise informs readiness for advanced technical leadership roles.
On-Device ML for Apple Platforms
Techniques and considerations for running machine learning models directly on devices (edge inference) on Apple platforms such as iPhone, iPad, and Vision Pro. Topics include Core ML integration, model optimization (quantization, pruning), on-device privacy and offline capabilities, performance tuning, and deployment strategies for mobile and AR devices.
On Device Privacy Architecture
Describe designing systems that minimize data collection and prioritize local processing on devices when appropriate. Topics include privacy preserving techniques such as federated learning and differential privacy, secure hardware enclaves and key management, local encryption and secure update mechanisms, data minimization and retention policies, and trade offs between functionality and privacy or regulatory requirements. Discuss engineering challenges of on device model inference including model size reduction, quantization and pruning for resource constrained devices, secure telemetry and diagnostics, and methods to measure privacy and utility trade offs.