Product Manager, Discover & Personalization (ML & AI)

Who We Are

Angel is the home of stories that amplify light. Through its platform, thousands of “Angel” investors choose which titles will be created, funded, and distributed. Angel allows creators and audiences to form passionate communities around their creative projects, making the story behind the story as important as the final project itself. Some of the studio's key projects-- The Sound of Freedom and Dry Bar Comedy --have earned billions of views around the world. Learn more at Angel.com

We’re looking for passionate team members who want to build world-class products that will reshape media over the coming decades. Learn more about:

Our North Star

Company Strategy

Team Principles

Join us and be part of stories that amplify light.

Summary/objective:

This role serves as the company's thought leader and strategic advisor on ML/AI-powered personalization, shaping not just our product roadmap but influencing how the entertainment industry thinks about algorithmic discovery.

• Serve as Angel's subject matter expert and internal advisor on recommendation systems, ML/AI personalization, and discovery best practices
• Define and evangelize the strategic vision for how AI should serve storytelling across the platform
• Advise leadership on ML/AI investment priorities, capability gaps, and competitive positioning
• Represent Angel's POV externally through industry connections, thought leadership content, and strategic partnerships
• Mentor and guide other PMs and cross-functional teams on ML/AI product principles and best practices

What You’ll Own

Discovery, Platform-Wide

• Define the strategic vision and long-term roadmap for Discover across all Angel surfaces, serving as the authoritative voice on how algorithmic systems should evolve to serve our mission”
• Define how algorithmic recommendations surface films, shows, specials, and community-driven moments
• Ensure Discover feels intelligent, intentional, and trustworthy across every surface

Machine Learning in Practice

• Operate at the center of ML- and AI-powered personalization
• Translate recommender models, rankings, embeddings, and signals into clear, testable product experiences
• Provide strategic guidance and technical leadership to data science and engineering teams, establishing best practices and architectural principles for ML-powered personalization across the organization

Metrics that Matter

• Set the strategic framework for how we measure discovery success, balancing short-term metrics with long-term platform health, user trust, and brand differentiation
• Design and run experiments, analyze cohorts, and turn insights into action
• Balance short-term optimization with long-term system health and user trust