The Personalization (PZN) team at Spotify seeks to understand the world of music, podcasts, and audiobooks to make great recommendations and enhance the listening experience through contextual storytelling.
Requirements
Strong background in machine learning, natural language processing, and generative AI, with experience in applying theory to develop real-world applications
Hands-on expertise with implementing end-to-end production ML systems at scale
Experience with production LLM scale based systems is a plus
Experience with incorporating human feedback to improve LLM based systems using technicals like DPO, KTO, and reinforcement fine-tuning
Experience with designing end-to-end tech specs and modular architectures for ML frameworks in complex problem spaces in collaboration with product teams
Experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, and cloud platforms like GCP or AWS
Responsibilities
Design, build, evaluate, and ship LLM based solutions that tell stories about our content and our users
Collaborate with cross functional teams spanning user research, design, data science, product management, and engineering to build new product features
Prototype new approaches and productionize solutions at scale for our hundreds of millions of active users
Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization
Be part of an active group of machine learning practitioners
Build and improve storytelling capabilities
Advance the mission to connect artists and fans in personalized and useful ways
Other
An experienced ML practitioner motivated to work on complex real-world problems in a fast-paced and collaborative environment
Ability to work within the North America region as long as we have a work location
Ability to work within the Eastern Standard time zone for collaboration
Bachelor's, Master's, or Ph.D. degree (not explicitly mentioned but implied)
Ability to request reasonable accommodations during the interview process
