Machine Learning Jobs in Glendale, CA

Build and operate large-scale backend services and offline ETL pipelines in Java/Scala, deploy and maintain ML models, improve performance and reliability, diagnose production issues, develop test automation, and collaborate with cross-functional teams to evolve service architecture.

Lead product strategy and roadmaps for Disney's content supply chain, coordinating across business, operations, engineering, and design to deliver scalable media platform capabilities, drive operational efficiency, and ensure successful adoption and execution of complex, cross-team initiatives.

The role involves leading technical initiatives in machine learning infrastructure, developing data pipelines, and collaborating across teams to enhance personalized news content delivery.

Lead cross-functional strategic initiatives for news and entertainment digital products. Define product vision, KPIs, and data-driven growth strategies; unify CRM/messaging and automation; leverage experimentation and AI/ML; advance experiment data governance; present to executives; and coach product teams to deliver scalable user experiences.

Serve as a technical SME in AML, sanctions, and third-party risk to support sales and client engagements. Lead RFPs, design API-based integrations, build data pipelines, scope bespoke projects, advise on data modeling and analytics, and drive solution positioning and product feedback across the Americas.

Lead development of ML infrastructure and pipelines for real-time personalization and recommendations. Own end-to-end initiatives from design to production, drive scalable training and inference, ensure observability and CI/CD, mentor engineers, and collaborate with product and editorial stakeholders to deliver low-latency, personalized news experiences at scale.

Lead the development of machine learning algorithms for personalization and recommendations in Disney's streaming products while collaborating with product and engineering teams.

Build and operate large-scale batch and streaming data pipelines and real-time feature infrastructure to support production ML. Develop ML-adjacent services, ensure reliability/observability, participate in on-call and incident response, and collaborate with ML, data, and platform teams to meet latency, availability, and data quality requirements.