Senior Machine Learning Engineer, Recommendations

About the role

We are looking for a Senior Machine Learning Engineer to join our Reco-Core team. As a key Machine Learning Engineer on this team, you will have a unique opportunity to shape and lead the ML practice for one of SoundCloud's most critical product areas. You will partner closely with ML, data, and backend engineers to design, build, and deploy the models that help millions of listeners discover their next favorite track within our vast and unique catalog of music and audio.

What you'll do

• Design, build, and deploy end-to-end machine learning solutions for recommendations, focusing on candidate generation, personalization, user and items modeling, and core ranking
• Take ownership of the full ML lifecycle, from prototyping and evaluation to deploying scalable models into production and monitoring their performance
• Strengthen and scale the foundation for ML engineering and MLOps within the Reco-Core team, defining best practices and building tooling to enable rapid iteration
• Partner with analysts and data scientists to translate research into production-ready systems
• Collaborate with other ML engineers to leverage shared infrastructure and knowledge
• Leverage agentic workflows and AI-assisted engineering as a force multiplier to work at 10x the speed of traditional methods

What you'll need

• Strong industry experience in a quantitative field (e.g., Computer Science, Statistics, Machine Learning) and/or a Ph.D./M.Sc. in a related discipline
• 3+ years of professional experience shipping large-scale ML models to production
• Strong engineering background in Python or Scala, with hands-on experience using data processing frameworks (e.g., Spark, Confluent) and vector search or nearest neighbor technologies
• Deep understanding of core recommendation concepts, including collaborative filtering, matrix factorization, deep learning for recommendations (e.g., two-tower architectures), and multi-objective ranking, with the ability to discuss trade-offs between different approaches
• Proven experience building and scaling ML infrastructure and tooling for model deployment, lifecycle management, and monitoring in a cloud environment (GCP, AWS, or Azure)
• Pragmatic approach to engineering efficiency, with a habit of leveraging modern AI-assisted workflows (e.g., Cursor, Claude Code) to accelerate development and research

What they offer

• Extensive relocation support including allowances, one way flights, temporary accommodation
• Creativity and Wellness benefit for gym membership, photography course or books
• Employee Equity Plan
• Generous professional development allowance
• Flexible vacation and public holiday policy with up to 35 days of PTO annually
• Free German courses at beginning, intermediate and advanced levels