As a Staff Machine Learning Engineer at Spotify, you'll drive the technical strategy for Search, focusing on ML for recommendations, collaborating with teams, and enhancing user experiences through advanced models and scalable architectures.
The Personalization team at Spotify makes deciding what to play next effortless and enjoyable for every listener. We aim to deeply understand music, podcasts, audiobooks, and videos to deliver exceptional recommendations that keep hundreds of millions of people engaged every day. Our work spans across experiences like Home, Search, curated playlists such as Discover Weekly and Daylist, and new innovations like AI DJ and AI Playlists.
Search is one of the most important entry points into Spotify’s ecosystem—powering how listeners find and rediscover music, podcasts, and audiobooks. Beyond retrieval, Search drives exploration and discovery, connecting fans with creators in new ways. Building world-class Search means tackling natural language understanding, personalization, and generative AI at massive scale. Generative AI is also revolutionizing Spotify’s product capabilities and technical infrastructure, with generative recommender systems, agent frameworks, and LLMs opening significant opportunities to meet diverse user needs, expand use cases, and gain richer insights into our content and users
As a Staff Machine Learning Engineer in Search, you’ll focus on recommender systems modeling at the intersection of generative recommenders and foundational understanding of user taste across music and talk content. You will define and execute the ML technical strategy for Search, building the next generation of Spotify’s recommendation systems, user representations, and supporting technical architecture. Join us and you’ll help millions of users discover and connect with the world’s audio content every day.
What You'll Do
- Define and drive the ML technical strategy for Search, focusing on retrieval, ranking, and generative approaches
- Build models that improve query understanding, personalization, and relevance across Spotify’s Search experiences
- Collaborate with a cross-functional agile team spanning user research, design, data science, product management, and engineering
- Prototype and productionize new modeling approaches at scale, serving hundreds of millions of users worldwide
- Lead high-impact projects from ideation through deployment, setting best practices for ML development, testing, evaluation, and experimentation
- Partner with tech leaders and stakeholders to influence priorities and ensure long-term scalability and impact
- Stay engaged with the broader ML and Search research community, applying emerging trends to Spotify’s challenges
Who You Are
- You have a strong background in machine learning and recommender systems, bridging research and user impact
- You have hands-on experience training and operating transformer models in production, or strong interest in doing so
- You have production experience developing large-scale ML systems in Java, Scala, Python, or similar languages. Experience with PyTorch or TensorFlow is a strong plus
- You are comfortable navigating ambiguity and leading high-impact projects from start to finish
- You’re a systems thinker and strong communicator who can align and influence technical and product stakeholders
- You care deeply about agile processes, data-driven development, and reliability
- You’re eager to apply emerging ML trends, particularly in LLMs and generative recommenders, to Spotify’s challenges
Where You'll Be
- We offer you the flexibility to work where you work best! For this role, you can be within the European region as long as we have a work location.
- This team operates within the GMT/CET time zone for collaboration.
- Excluding France due to on-call restrictions.
Top Skills
Java
Machine Learning
Python
PyTorch
Recommender Systems
Scala
TensorFlow
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