Senior Machine Learning Engineer
Personalization

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