Staff Machine Learning Engineer, Personalization

About the Role

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.

Surfaces Moments is an ML team within the Personalization Mission focused on creating moment-based experiences across Spotify surfaces. The team owns and evolves experiences such as the Home Shortcuts experience. By combining machine learning, recommendation systems, and product thinking, the team delivers personalized experiences to millions of listeners worldwide.

As a Staff Machine Learning Engineer, you will help shape the future of personalized discovery and engagement. You will work across recommendation systems, large language models, and production-scale ML infrastructure.

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 serving 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 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 supporting personalization at Spotify scale.
• Drive technical direction in ambiguous problem spaces and contribute to long-term personalization system architecture.
• Mentor and support other machine learning engineers.

Who You Are

• 8+ years of experience building and deploying machine learning systems in production environments.
• Deep expertise in recommendation systems, ranking models, personalization, or large-scale content discovery platforms.
• Strong proficiency in Python and hands-on experience building machine learning systems with PyTorch.
• Experienced with large language model training, fine-tuning, evaluation, and optimization techniques (e.g., SFT, distillation, LoRA).
• Experience with large-scale inference systems and understanding of latency, reliability, and cost optimization challenges.
• Effective communicator across technical and non-technical audiences, with ability to influence technical decisions broadly.
• Experienced in designing, executing, and interpreting online experiments and A/B tests.
• Experience operating distributed ML workloads (e.g., Ray, FSDP, HSDP).
• Experience building and maintaining data pipelines and orchestration workflows using tools like Flyte, Airflow, BigQuery, and cloud storage platforms.