Director, Science & Machine Learning - Search & Voice

$203,800 - $280,000/Yr

Sirius XM Radio - Oakland, TX

Full-time - Senior

Oakland, TX

Broadcasting and Content Providers

About the position

SiriusXM is seeking a senior applied science leader to join our Science and Machine Learning organization. In this pivotal role, you will lead a team of applied scientists and collaborate closely with engineering and product teams to enhance the search and voice experience across our suite of products, including SiriusXM and Pandora. The primary mission of your team will be to improve the discovery of audio content—such as music, talk shows, and podcasts—through both text and voice search functionalities. Your responsibilities will include working cross-functionally with business and product owners to identify and translate requirements into scientific problems. You will lead, manage, and inspire a team of applied scientists to develop algorithms and machine learning models aimed at improving search quality across our consumer-facing products. Collaboration with the engineering team will be essential for the implementation and deployment of new algorithms and machine learning models into production. As a technical leader, you will drive project execution and define a long-term technical roadmap for your team. You will also contribute to the broader vision and long-term scientific roadmap of the organization, fostering a culture of innovation, impact, collaboration, and inclusion within your team and the wider Science and Machine Learning organization. Additionally, you will prepare and present executive summaries of results to stakeholders in product, engineering, and business organizations, while driving technical innovation through active research and the application of state-of-the-art machine learning and algorithmic technologies. Your role will also involve generating intellectual property and enhancing the research and tech brand of the organization.

Responsibilities

• Work cross functionally with business and product owners to identify and translate requirements to scientific problems.
• Lead, manage and inspire a team of applied scientists to develop algorithms and machine learning models to improve search quality across our consumer facing products.
• Collaborate with the engineering team on implementation and deployment of new algorithms and machine learning models to production.
• Provide technical leadership, driving project execution, and defining a long-term technical roadmap for the team.
• Contribute to the broader vision and long term scientific roadmap of the organization.
• Foster a culture of innovation, impact, collaboration and inclusion within your team and the broader Science and Machine Learning organization.
• Prepare and present executive summaries of results to stakeholders in product, engineering and business organizations.
• Drive technical innovation through active research and application of state of the art machine learning and algorithmic technologies.
• Actively participate in generating intellectual property and strengthening the research and tech brand of the organization.

Requirements

• MS, or PhD in Computer Science, Computer Engineering, Mathematics, Electrical Engineering, Physics, or a related field.
• 10+ years of relevant experience in research and development of machine learning systems and algorithms including 3+ years leading teams in search domain.
• Experience with ML/AI algorithms, deep learning, or natural language processing (NLP).
• Excellent communication skills and the ability to lead multi-functional efforts.
• Ability to work in ambiguous environments.
• Excellent written and verbal communication skills.
• Ability to work independently and in a team environment.
• Commitment to 'internal client' and customer service principles.
• Willingness to take initiative and to follow through on projects.
• Must have legal right to work in the U.S.

Benefits

• Base salary range of $203,800 to $280,000 depending on skills, qualifications, and experience.
• Eligibility for discretionary short-term and long-term incentives.