Machine Learning Engineer
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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
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