Staff Machine Learning Engineer, Home Surfaces

Job description

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. Surfaces Moments is a ML team within the Personalization Mission focused on creating moment-based experiences across Spotify surfaces. The team owns and evolves the experiences that help listeners quickly connect with the content that matters most to them, including the Home Shortcuts experience and the underlying intelligence that powers it. By combining cutting-edge machine learning, recommendation systems, and product thinking, the team delivers highly relevant, personalized experiences to millions of listeners around the world. As a Staff Machine Learning Engineer, you will 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 that delight users and drive meaningful impact. This role is ideal for someone who enjoys taking models from research to production, driving technical direction in ambiguous problem spaces, and solving complex personalization challenges at global scale. ### 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 personalization systems. - Mentor and support other machine learning engineers, helping raise the bar across the team. ### Who You Are - You have 8+ years of experience building and deploying machine learning systems in production environments. - You have deep expertise in recommendation systems, ranking models, personalization, or large-scale content discovery platforms. - You have strong proficiency in Python and hands-on experience building machine learning systems with PyTorch. - You are experienced 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 deeply about creating high-quality user experiences through thoughtful application of machine learning. - You communicate effectively across technical and non-technical audiences, and you 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 are experienced 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.