Description
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 complete 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, pioneering architectures
- You have a collaborative approach, 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 crafting 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 North America region as long as we have a work location.
- This team operates within the EST time zone for collaboration.
The United States base range for this position is $138,250 - $197,500 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. This range encompasses multiple levels. Leveling is determined during the interview process. Placement in a level depends on relevant work history and interview performance. These ranges may be modified in the future.
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