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