SoundCloud empowers artists and fans to connect and share through music. Founded in 2007, SoundCloud is an artist-first platform empowering artists to build and grow their careers by providing them with the most progressive tools, services, and resources. With over 400+ million tracks from 40 million artists, the future of music is SoundCloud. We are looking for a Senior Machine Learning Engineer to join our Search team. As the founding 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 data science and engineering teams to design, build, and deploy the models that help millions of listeners discover our vast and unique catalog of music and audio.
Key Responsibilities:
1. Design, build, and deploy end-to-end machine learning solutions for search, focusing on ranking, relevance, and query understanding (NLP)
2. Take ownership of the full ML lifecycle, from prototyping and evaluation to deploying scalable models into production and monitoring their performance
3. Strengthen and scale the foundation for ML engineering and MLOps within the Search team, defining best practices and building tooling to enable rapid iteration
4. Partner with analysts and data scientists to translate research into production-ready systems
5. Collaborate with other ML engineers to leverage shared infrastructure and knowledge
Experience and Background:
6. A Ph.D. or M.Sc. in a quantitative field (e.g., Computer Science, Statistics, Machine Learning) or equivalent industry experience
7. 3+ years of professional experience shipping large-scale ML models to production
8. Strong engineering background in Python or Scala, with hands-on experience using data processing frameworks (e.g., Spark, BigQuery) and search technologies (e.g., Elasticsearch)
9. Deep understanding of core search concepts, including indexing, retrieval, and Learning to Rank (LTR), and the ability to discuss trade-offs between different approaches
10. Proven experience building and scaling ML infrastructure and tooling for model deployment, lifecycle management, and monitoring in a cloud environment (GCP, AWS, or Azure)