Staff Machine Learning Engineer, Home Surfaces

The Personalization team at Spotify makes deciding what to play next easier and more enjoyable for every listener. As a Staff Machine Learning Engineer on the Surfaces Moments team, you will help shape the future of personalized discovery and engagement at Spotify.

You will 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.

Responsibilities

• 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.

Requirements

• 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.
• Experience with large language model training, fine-tuning, evaluation, and optimization techniques including SFT, distillation, and LoRA.
• Worked with large-scale inference systems and understand the challenges of latency, reliability, and cost optimization.
• Care deeply about creating high-quality user experiences through thoughtful application of machine learning.
• Communicate effectively across technical and non-technical audiences, and influence technical decisions beyond your immediate team.
• Know how 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.
• Experienced building and maintaining data pipelines and orchestration workflows using technologies such as Flyte, Airflow, BigQuery, and cloud-based storage platforms.

Benefits

• Health insurance
• Six month paid parental leave
• 401(k) retirement plan
• Monthly meal allowance
• 23 paid days off
• 13 paid flexible holidays
• Paid sick leave