Machine Learning Jobs in Glendale, CA

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.

Develop and maintain software components for ML-assisted media content inspection pipelines supporting automated asset validation, enrichment, and analysis. Integrate AI and machine-learning services, work with computer vision and LLM workflows, support cloud-based systems, and assist with production monitoring and troubleshooting. The role requires collaboration, testing, documentation, and continuous development of software, cloud, and introductory machine-learning skills.

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.

Own the technical architecture and strategic direction of Disney’s News & Entertainment machine learning platform. Lead personalization, recommendation systems, ML infrastructure, data pipelines, model training, online serving, reliability, incident response, and platform standards across major media brands. Drive metrics-based business outcomes, cross-organizational engineering initiatives, technology adoption, architecture reviews, and mentorship of senior engineers.

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.

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.