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