What You'll Do

Contribute to the design, build, evaluation, shipping, and refinement of systems that improve Spotify’s promotional performance with hands-on ML development
Collaborate with a multidisciplinary team to optimize machine learning models for production use cases, ensuring they are highly efficient, scalable, and consistently meet well-defined success criteria
Influence the technical design, architecture, and infrastructure decisions to support new and diverse machine learning architectures.
Work with Data and ML Engineers to support transitioning machine learning models from research and development into production
Implement and monitor model success metrics, diagnose issues, and contribute to an on-call schedule to maintain production stability.

Who You Are

Experience implementing ML systems at scale in Java, Scala, Python or similar languages as well as experience with ML frameworks such as TensorFlow, PyTorch, etc.
Understanding of how to bring machine learning models from research to production and comfortable working with innovative, cutting-edge architectures.
Collaborative mindset, enjoy working closely with research scientists, machine learning engineers, and data engineers to innovate and improve models.
Experience in optimizing machine learning models for production use cases
Preferably experience with data pipeline tools like Apache Beam, Scio, and cloud platforms like GCP
Some exposure to causal ML models, including things like counterfactuals.
Familiar with creating model success metric dashboards, diagnosing production issues, and willing to take part in an on-call schedule to maintain performance.