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SiriusXM

Senior ML Scientist, Streaming Fraud and Content Understanding

SiriusXM, Atlanta, Georgia, United States, 30383

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Senior ML Scientist, Streaming Fraud and Content Understanding

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SiriusXM

Who We Are

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. Our vision is to shape the future of audio, where everyone can be effortlessly connected to the voices, stories and music they love wherever they are.

How You’ll Make An Impact As a Senior Machine Learning Scientist, you will play a critical role in protecting our platform from streaming fraud, unfair royalty payouts, and degraded user experiences. You will research and develop ML models to detect anomalous user behavior while also building foundational ML/AI systems that deeply understand our content. You will closely collaborate with partners in Engineering, Content Licensing, and Product to productionize systems that mitigate fraud, uphold the integrity of royalty payouts, and surface high-quality content to our listeners.

What You’ll Do

Frame business goals as machine learning problems across Pandora and SiriusXM and own their execution.

Research, design, and build high-impact machine learning models to combat streaming fraud at scale and extend SiriusXM’s content understanding capabilities.

Develop content understanding models using ML/AI techniques that power downstream systems across listener experiences, royalty processing, and catalog management.

Create new signals and pipelines to better model user behavior, content consumption patterns, and behavioral anomalies.

Collaborate with cross-functional partners including Engineering, Product, Content Licensing, and Curation.

This role does not have direct supervisory responsibilities.

What You’ll Need

A Ph.D. or M.S. in a quantitative field and 3-5 years of relevant work experience

3+ additional years of research and development experience in real-world content understanding and/or fraud detection systems.

Deep understanding of supervised and unsupervised machine learning, anomaly detection, and graph-based techniques

Hands-on experience with LLMs, including retrieval-augmented generation (RAG) pipelines, vector stores, and orchestration frameworks

Experience with large-scale data and production ML pipelines (e.g., TensorFlow/PyTorch, Airflow, Spark, cloud platforms like AWS/GCP)

Excellent written and verbal communication skills, with the ability to effectively advocate technical solutions to scientists, engineers, and product audiences.

Passion for data-driven research, development, and experimentation.

Self-motivated, growth-oriented, and driven to pursue solutions to challenging problems.

Must have legal right to work in the U.S.

At SiriusXM, we carefully consider a wide range of factors when determining compensation, including your background and experience. These considerations can cause your compensation to vary. We expect the base salary for this position to be in the range of $130,000 and $217,000 and depend on your skills, qualifications, and experience. Additionally, this role might be eligible for discretionary short-term and long-term incentives. We encourage all interested candidates to apply.

Our goal at SiriusXM is to provide and maintain a work environment that fosters mutual respect, professionalism and cooperation. 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.

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