Staff Machine Learning Engineer, Personalization

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

• 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 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.