Machine Learning Engineer

Machine Learning Engineer

The Music Promotion team is building products that allow creators to promote their work to reach new audiences and create lasting connections with their fans. We're looking for a Machine Learning Engineer to help us build systems that more accurately understand the performance that promotion can have, giving customers actionable insights for building their promotion strategies, whether it's a DIY artist or an industry-facing partner.

As an ML Engineer, you will help execute on strategies for understanding the factors that play a role in the performance of promoted tracks across the globe. You'll build data-driven solutions, as well as effective online and offline strategies to efficiently iterate and evaluate model approaches. You'll have access to a growing list of datasets, features and ML infrastructure to continually experiment and improve the model-based approach.

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

• You have 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.
• You have an understanding of how to bring machine learning models from research to production and are comfortable working with innovative, cutting-edge architectures.
• You have a collaborative mindset, enjoy working closely with research scientists, machine learning engineers, and data engineers to innovate and improve models.
• You have experience in optimizing machine learning models for production use cases
• You preferably have experience with data pipeline tools like Apache Beam, Scio, and cloud platforms like GCP
• You have some exposure to causal ML models, including things like counterfactuals.
• You are familiar with creating model success metric dashboards, diagnosing production issues, and are willing to take part in an on-call schedule to maintain performance.

Where You'll Be We offer you the flexibility to work where you work best! For this role, you can be within the EST time zone as long as we have a work location.

The United States base range for this position is $170,000 - $212,000 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. These ranges may be modified in the future.

Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what's playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It's in our differences that we will find the power to keep revolutionizing the way the world listens.

At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we're here to support you in any way we can.