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