Senior Machine Learning Engineer (RecSys)

We are building an AI-powered music platform that’s transforming how people create, explore, and experience music. Our product leverages cutting-edge AI technologies to provide personalized music recommendations and unique features tailored to every music enthusiast. As we continue to grow, we’re looking for a Senior Machine Learning Engineer to design, build, and scale recommendation systems that deliver highly relevant, personalized experiences to our users. You will work on large-scale user interaction data, develop retrieval and ranking models, and take them from experimentation to production. What You’ll Do • Design and implement retrieval and ranking architectures for personalized recommendations • Work with large-scale user behavior and content data to extract meaningful signals • Build end-to-end ML systems: data processing, feature engineering, training, evaluation, deployment, monitoring • Run A/B tests and offline evaluations to measure model impact and guide improvements • Collaborate with product and engineering teams to align recommendations with business goals • Continuously monitor model performance What We’re Looking For • Strong hands-on experience building recommendation systems or ranking models • Deep understanding of machine learning fundamentals and evaluation methodologies • Experience working with large-scale data (SQL, Spark, or distributed data systems) • Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow) • Understanding of core ML concepts: supervised/unsupervised learning, evaluation metrics, feature engineering • Experience deploying ML models to production and maintaining them over time • Ability to balance experimentation with production reliability Nice to Have • Experience with real-time recommendation systems • Knowledge of search / information retrieval systems • Familiarity with feature stores, model monitoring, and ML infrastructure • Experience in media, music, or consumer-facing personalization products Why Join Us • Work on high-impact ML systems used by real users at scale • Ownership over meaningful technical decisions, from modeling to production • Collaborative, product-driven environment with strong engineering culture • A supportive and dynamic startup culture where your ideas and contributions truly matter • Opportunities for growth, learning, and shaping the future of our recommendation stack