Staff Machine Learning Engineer, Personalization

Spotify is a leading music streaming service, and they are seeking a Staff Machine Learning Engineer to join their Personalization team. In this role, you will shape the future of personalized discovery and engagement by building and optimizing machine learning models that enhance user experiences.

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
• 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