About the 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 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
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
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
