Personalization Senior Software Engineer

9 hours ago


Stockholm, Stockholm, Sweden Spotify Full time

About Us

At Spotify, our goal is to make music and podcast discovery easier and more enjoyable for millions of users worldwide.

The Personalization team plays a crucial role in achieving this vision by building innovative features like Discover Weekly and AI DJ.

We're seeking a seasoned Senior Machine Learning Engineer to join our team and contribute to the development of cutting-edge personalized sessions with generative voice AI.

As part of our Text-to-Speech (TTS) team, Speak, you'll work closely with researchers and engineers to create generated voice audio that enriches users' experience of music and podcast recommendations.

Key Responsibilities:

  • 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.

Requirements:

  • Passion for speech, audio, and/or generative machine learning.
  • World-class expertise in optimizing machine learning models for production use cases, and extensive experience with machine learning frameworks like Pytorch.
  • Experience in building efficient, scalable infrastructure to serve machine learning models, and managing Kubernetes clusters in multi-region setups.
  • Strong understanding of how to bring machine learning models from research to production and comfort in working with innovative, cutting-edge architectures.
  • Familiarity with writing logging metrics and diagnosing production issues, and willingness to take part in an on-call schedule to maintain uptime and performance.
  • Collaborative mindset, enjoying working closely with research scientists, machine learning engineers, and backend engineers to innovate and improve model deployment pipelines.
  • Thriving 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.


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