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Senior Machine Learning Engineer - Personalization
Spotify
Job Location
Job Summary
The Personalization team at Spotify is seeking a skilled engineer to optimize machine learning models for production use cases. The role involves collaborating with a multidisciplinary team to design and build efficient serving infrastructure, leading the transition of machine learning models from research to production, and implementing scalable Kubernetes clusters. The ideal candidate has expertise in Pytorch, experience with low-level machine learning libraries, and a strong understanding of how to bring machine learning models from research to production. This is a remote opportunity within the European region, offering flexible work arrangements and a chance to contribute to innovative projects.
The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Discover Weekly to AI DJ, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations – and providing valuable context – to each and every one of them.
Do you want to help Spotify invent new personalized sessions with generative voice AI to delight users? In this role, you’ll work with Spotify’s Text-to-Speech (TTS) team, Speak, to create generated voice audio that enriches users’ experience of music and podcast recommendations.
What You'll Do
Collaborate with a multidisciplinary team to optimize machine learning models for production use cases, ensuring they are highly efficient and scalable
Design and build efficient serving infrastructure for machine learning models that supports large-scale deployments across different regions
Optimize machine learning models in Pytorch or other libraries for real-time serving and production applications
Lead the effort to transition machine learning models from research and development into production, working closely with researchers and machine learning engineers
Build and maintain scalable Kubernetes clusters to manage and deploy machine learning models, ensuring reliability and performance
Implement and monitor logging metrics, diagnose infrastructure issues, and contribute to an on-call schedule to maintain production stability
Influence the technical design, architecture, and infrastructure decisions to support new and diverse machine learning architectures
Collaborate with stakeholders to drive forward initiatives related to the serving and optimization of machine learning models at scale.
Who You Are
You have a passion for speech, audio and/or generative machine learning
You have world-class expertise in optimizing machine learning models for production use cases, and extensive experience with machine learning frameworks like Pytorch
You are experienced in building efficient, scalable infrastructure to serve machine learning models, and managing Kubernetes clusters in multi-region setups
You have a strong understanding of how to bring machine learning models from research to production and are comfortable working with innovative, cutting-edge architectures
You are familiar with writing logging metrics and diagnosing production issues, and are willing to take part in an on-call schedule to maintain uptime and performance
You have a collaborative mindset, enjoy working closely with research scientists, machine learning engineers, and backend engineers to innovate and improve model deployment pipelines
You thrive in environments that require solving complex infrastructure challenges, including scaling and performance optimization
Experience with low-level machine learning libraries (e.g., Triton, CUDA) and performance optimization for custom components is a bonus
Where You'll Be
We offer you the flexibility to work where you work best! For this role, you can be within the European region as long as we have a work location.
This team operates within the GMT/CET time zone for collaboration.
Excluding France due to on-call restrictions.
