Staff Machine Learning Engineer, Home Surfaces

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them. The Surfaces Podcasts team owns podcast recommendations across some of Spotify’s most visible surfaces, including Home and the Now Playing view. We build and improve the recommendation systems that help listeners discover their next favorite show or episode while helping creators reach the right audiences. As a Staff Machine Learning Engineer, you’ll help shape the future of personalized discovery and engagement at Spotify. You’ll work at the intersection of recommendation systems, large language models, and production-scale machine learning infrastructure to build experiences for listeners at global scale. You’ll take models from research to production, help set technical direction in ambiguous problem spaces, and solve complex personalization challenges across Spotify. What You'll Do: Own and improve the machine learning models and systems that power the Home feed, including the Shortcuts experience. Design, build, and ship personalized recommendations that serve millions of Spotify listeners globally. Build content recommendation systems for emerging agentic and AI-powered user experiences. Train, fine-tune, evaluate, and optimize large language models using techniques such as supervised fine-tuning (SFT), distillation, and parameter-efficient training approaches. Partner closely with Product Managers, Engineers, Data Scientists, and Designers to define and execute experimentation strategies. Drive A/B testing, monitoring, model evaluation, and continuous optimization of recommendation quality, reliability, and cost efficiency. Improve ML platform capabilities, data pipelines, and production systems that support personalization at Spotify scale. Drive technical direction in ambiguous problem spaces and contribute to the long-term architecture of our personalization systems. Mentor and support other Machine Learning Engineers, sharing knowledge and helping strengthen engineering practices across the team. Who You Are: You have deep experience building and deploying machine learning systems in production environments. You have deep experience with recommendation systems, ranking models, personalization, or large-scale content discovery platforms. You are proficient in Python and have hands-on experience building machine learning systems with PyTorch. You have experience with large language model training, fine-tuning, evaluation, and optimization techniques, including SFT, distillation, and LoRA. You have worked with large-scale inference systems and understand the challenges of latency, reliability, and cost optimization. You care about creating high-quality user experiences through thoughtful applications of machine learning. You communicate effectively with technical and non-technical partners and can influence technical decisions beyond your immediate team. You know how to design, execute, and interpret online experiments and A/B tests to improve user outcomes. You have experience operating distributed machine learning workloads using technologies such as Ray, FSDP, HSDP, or similar frameworks. You have experience building and maintaining data pipelines and orchestration workflows using technologies such as Flyte, Airflow, BigQuery, and cloud-based storage platforms. Where You'll Be: We offer you the flexibility to work where you work best! For this role, you can be within the North Americas region as long as we have a work location. This team operates within the Eastern Standard time zone for collaboration.