Senior Staff Machine Learning Scientist, Recommendations

About SiriusXM

SiriusXM, together with its brands Pandora, AdsWizz, Simplecast, and SiriusXM Connect, leads the evolution of audio entertainment and services. We deliver compelling subscription and ad-supported audio experiences accessible in cars, homes, and via connected devices. Our vision is to shape the future of audio, connecting listeners effortlessly with their favorite voices, stories, and music anytime and anywhere.

Our diverse team combines rising talent and industry veterans to craft world-class programming and technology. SiriusXM’s commanding presence in North America includes over 150 million listeners across paid and free tiers, providing music, sports, comedy, news, talk, live events, and podcasts. Pandora is the largest ad-supported audio streaming service in the US. Subsidiaries Simplecast and AdsWizz provide podcast hosting, distribution, analytics, and monetization. SiriusXM Media maximizes advertising reach through scaled, cross-platform sales and advanced ad tech. Additionally, SiriusXM extends satellite radio and connected vehicle services in Canada.

Role Impact

As a Senior Staff Machine Learning Scientist focused on Recommendations, you will influence how tens of millions discover their next audio content choice. You will advance large-scale recommender systems, enhancing personalization and ranking models for a wide range of audio formats including channels, radio stations, shows, podcasts, sports, and news within our Discover experience. This position integrates deep learning, real-time systems, and measurable product impact.

Key Responsibilities

• Develop and enhance personalized recommendation models using traditional methods, deep learning, transformer architectures, and AI-driven techniques tailored to diverse audio content.
• Innovate new methods to enable cross-domain recommendations, live content suggestions, multi-stakeholder prioritization, and improve the explainability and interactivity of recommendations.
• Continuously assess and optimize model effectiveness through offline metrics and A/B testing, ensuring alignment with strategic business objectives and superior user experience.
• Implement and promote best practices in machine learning development, deployment, and performance monitoring.
• Collaborate across functions with product managers, backend and data engineers, and ML engineers to define and execute roadmaps spanning multiple recommendation surfaces.
• Stay current with advances in ML and AI, integrating cutting-edge methods to solve complex recommendation and ranking challenges and helping to set technical direction and vision.
• Lead the technical strategy for all recommendation and personalization initiatives company-wide.
• Mentor junior scientists, perform comprehensive code reviews, and cultivate a culture of knowledge sharing and continuous skill enhancement.

Required Qualifications

• Master's degree in a quantitative field required; Ph.D. preferred.
• Seven or more years of professional experience building recommender systems and scalable ML pipelines using Python, Java, Scala, or similar languages.
• Strong understanding of production ML lifecycle including model versioning, tracking, deployment, monitoring, and usage of feature stores.
• Expertise in contemporary machine learning methods for recommendations, such as transformer models, deep learning ranking systems, and large language model or agentic recommendation strategies.
• Excellent communication skills to clearly present technical solutions to both technical and product-focused audiences.
• Self-driven and growth-oriented mindset with motivation to develop innovative solutions to challenging problems.

Diversity and Inclusion

SiriusXM fosters a professional environment built on respect and cooperation, supporting equal opportunity regardless of race, religion, gender identity, veteran status, disability, age, sexual orientation, or any legally protected characteristic.

Additional Notes

The responsibilities and requirements are subject to modification at the company's discretion without notice.