Staff Machine Learning Scientist, Recomendations

About SiriusXM

SiriusXM and its brands (Pandora, SiriusXM Media, AdsWizz, Simplecast, and SiriusXM Connect) are leading a new era of audio entertainment and services by delivering the most compelling subscription and ad-supported audio entertainment experience for listeners — in the car, at home, and anywhere on the go with connected devices.

SiriusXM is the leading audio entertainment company in North America, and the premier programmer and platform for subscription and digital advertising-supported audio products. SiriusXM's platforms collectively reach approximately 150 million listeners, the largest digital audio audience across paid and free tiers in North America, and deliver music, sports, talk, news, comedy, entertainment and podcasts. Pandora, a subsidiary of SiriusXM, is the largest ad-supported audio entertainment streaming service in the U.S. SiriusXM's subsidiaries Simplecast and AdsWizz make it a leader in podcast hosting, production, distribution, analytics and monetization. The Company's advertising sales organization, which operates as SiriusXM Media, leverages its scale, cross-platform sales organization and ad tech capabilities to deliver results for audio creators and advertisers. SiriusXM also offers connected vehicle services to automakers.

The Role

At SiriusXM and Pandora, recommendations are central to how listeners discover and engage with content. As a Staff Machine Learning Scientist, Recommendations, you will lead the development of advanced recommendation and personalization systems that power content discovery across a diverse audio ecosystem. Your work will directly influence how millions of listeners connect with music, podcasts, channels, sports, news, and emerging content experiences.

You will drive complex machine learning initiatives that advance recommendation quality, personalization effectiveness, and user engagement. Operating with a high degree of independence, you will influence technical direction within the recommendations domain, guide cross-functional efforts, and help shape the evolution of large-scale recommendation systems through deep expertise in machine learning, ranking, and personalization technologies.

This role does not have direct supervisory responsibilities.

Key Responsibilities

• Lead the design, development, deployment, and continuous improvement of machine learning systems that support recommendation, ranking, and personalization experiences across multiple listener touchpoints.
• Drive technical execution of complex initiatives involving personalized recommendations, content ranking, cross-domain discovery, real-time recommendations, and user engagement optimization.
• Apply advanced machine learning techniques, including deep learning, transformer architectures, representation learning, reinforcement learning, and generative AI approaches to solve challenging recommendation and personalization problems.
• Own major recommendation initiatives from problem definition through deployment, experimentation, monitoring, and continuous optimization in production environments.
• Establish scalable approaches for recommendation evaluation, experimentation, model performance measurement, and business impact assessment.
• Collaborate closely with Product, Engineering, Analytics, Data Science, and Content teams to identify opportunities, prioritize investments, and deliver impactful machine learning solutions.
• Identify opportunities to improve recommendation quality, user engagement, personalization effectiveness, and content discovery through innovative machine learning approaches.
• Influence technical direction within the recommendations domain through architecture reviews, technical guidance, and development of best practices.
• Mentor scientists and engineers, providing guidance on recommendation methodologies, experimentation strategies, and production machine learning practices.
• Contribute to improving machine learning infrastructure, experimentation platforms, feature engineering workflows, and recommendation system scalability.
• Serve as a trusted technical resource for complex recommendation and personalization challenges and contribute to cross-functional decision making.

Requirements

Education

• M.S. in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Applied Mathematics, or a related quantitative field required; Ph.D. preferred.

Experience

• 6+ years of production experience developing and deploying machine learning systems at scale, including experience leading complex recommendation, ranking, personalization, or user engagement initiatives.

Technical Expertise

• Strong experience with recommendation systems, ranking models, personalization techniques, deep learning methodologies, transformer architectures, and modern machine learning approaches.

Production ML

• Demonstrated experience taking machine learning solutions from concept through deployment, monitoring, optimization, and operational support in large-scale environments.

Leadership

• Proven ability to lead technical initiatives, influence stakeholders, and drive results across cross-functional teams without direct authority.

Problem Solving

• Experience solving complex and ambiguous technical challenges while balancing business objectives, scalability requirements, and user experience considerations.

Communication

• Strong written and verbal communication skills with the ability to communicate complex technical concepts to technical and non-technical audiences.

Collaboration

• Demonstrated success partnering across product, engineering, analytics, and business organizations to deliver measurable outcomes.

Mindset

• Self-motivated, proactive, and comfortable owning initiatives from strategy through execution while continuously advancing technical excellence and innovation.

Equal Opportunity

SiriusXM is an equal opportunity employer that does not discriminate on the basis of actual or perceived race, creed, color, religion, national origin, ancestry, alienage or citizenship status, age, disability or handicap, sex, gender identity, marital status, familial status, veteran status, sexual orientation or any other characteristic protected by applicable federal, state or local laws.

The requirements and duties described above may be modified or waived by the Company in its sole discretion without notice.