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At SiriusXM and Pandora, recommendations sit at the core of the user experience. As a Senior Staff Machine Learning Scientist, Recommendations, you will shape how tens of millions of listeners discover what to listen to next and push the frontier of large-scale recommender systems. Your work will directly define and advance the next generation of ranking and personalization models across our diverse audio content (channels, radio stations, shows, podcasts, live sports, news, talk, etc.) in our Discover home page experiences. You will operate at the intersection of deep learning, real-time systems, and measurable product impact.
Job Responsibility
Design, build, and improve traditional, deep-learning, transformer-based, and AI-based personalised recommendation models for various audio-content recommendation tasks
Drive the development of novel methods to enable cross-domain recommendations, live on-the-air content recommendations, multi-stakeholder recommendations, and improved recommendation explainability and interactivity
Continuously monitor, evaluate, optimise, and improve model performance, ensuring alignment with business goals and high-quality user experiences via offline metrics and A/B testing
Establish and scale best practices for ML development, deployment, and evaluation
Lead cross-functional efforts with product, backend engineers, data engineers, and machine learning engineers to define and drive roadmaps across multiple recommendation and personalisation surfaces
Stay up-to-date with the latest techniques in machine learning and apply these advancements to solve complex problems in recommendations and ranking, helping set the technical direction and vision for personalisation in a rapidly evolving ML and AI landscape
Own and influence the technical strategy for recommendation and personalisation systems across the organisation
Elevate the team’s capabilities by mentoring junior members, conducting thorough code reviews, and fostering an environment of shared knowledge and continuous learning
Requirements
M.S. in a quantitative field required
Ph.D. preferred
7+ years production experience implementing recommender systems, machine learning pipelines, models at scale in Python, Java, Scala, or similar languages
Production ML: Knowledge of production ML best practices such as model versioning, tracking, deployment, monitoring
serving concepts such as feature stores, etc.
ML Concepts: Expertise with modern ML approaches for recommendations such as transformers, other deep-learning based recommender/ranking approaches, and LLM/agentic recommendation approaches
Soft Skills: Excellent written and verbal communication skills, with the ability to effectively advocate technical solutions to scientists, engineers, and product audiences
Mindset: Self-motivated, growth-oriented, and driven to pursue solutions to challenging problems