
Personalization Senior Software Engineer
9 hours ago
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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