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