Staff Machine Learning Engineer, Home Surfaces

About the Role

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

• Deep experience building and deploying machine learning systems in production environments.
• Deep experience with recommendation systems, ranking models, personalization, or large-scale content discovery platforms.
• Proficient in Python and hands-on experience building machine learning systems with PyTorch.
• Experience with large language model training, fine-tuning, evaluation, and optimization techniques, including SFT, distillation, and LoRA.
• Experience with large-scale inference systems and understanding of latency, reliability, and cost optimization challenges.
• Passion for creating high-quality user experiences through thoughtful applications of machine learning.
• Effective communication with technical and non-technical partners, with the ability to influence technical decisions beyond your immediate team.
• Proven ability to design, execute, and interpret online experiments and A/B tests to improve user outcomes.
• Experience operating distributed machine learning workloads using technologies such as Ray, FSDP, HSDP, or similar frameworks.
• 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

• Flexibility to work remotely anywhere within the North Americas region where Spotify has a work location.
• The team operates within the Eastern Standard Time (EST) zone for collaboration.

Accommodations

Spotify provides reasonable accommodations during the interview process. If you need accommodations at any stage of the application or interview process, please let the team know.