About this role
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 DoDesign 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 ForStrong 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 HaveExperience 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 UsWork 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