Principal Machine Learning Engineer (Content ML)

Join Snap as a Principal Machine Learning Engineer in the Content ML team. Lead the vision and roadmap for large-scale recommendation systems, collaborate with cross-functional teams, and advance the ML tech stack for recommendations. Enjoy a comprehensive benefits package, including generous time off, medical coverage, and retirement plans.

• Lead the vision and roadmap for Snap’s large-scale recommendation systems, elevating content discovery and personalization across various platforms.
• Technically lead a group of talented engineers to operate and scale the existing recommender system, and work with cross-team partners to design the next-gen recommender system.
• Advocate for and implement best practices in availability, scalability, experimentation rigor, operational excellence, and cost management.
• Skilled at solving complex technical challenges, influencing architecture decisions, and driving execution across multi-stakeholder environments
• Strong foundation in machine learning, deep learning, and large-scale recommendation/ranking systems
• 2+ years of experience with technical leadership or acting as the domain-expert to a technical organization
• 9+ years of post-Bachelor’s machine learning experience; or a Master’s degree in a technical field + 8+ years of post-grad ML experience; or a PhD in a related technical field + 5+ years of post-grad ML experience
• Deep understanding of RecSys architectures and experience applying them to real-world production systems
• Strong collaboration, communication, and mentorship abilities
• Experience leading teams or roadmaps focused on recommendations and/or personalization
• Ability to design, train, deploy, and optimize state-of-the-art machine learning models for performance, reliability, and scale
• Experience developing and shipping performant and scalable machine learning models for recommendation or ranking use cases
• Excellent programming and software engineering skills, with an emphasis on clean design and production-readiness
• Ability to quickly learn new technologies and apply them effectively in ambiguous problem spaces
• Experience contributing to AI publications
• Experience partnering with cross-functional executives and management across a globally distributed organization and exercising sound judgment
• Background in integrating recommendation models into production pipelines
• Experience with TensorFlow, PyTorch, or related deep learning frameworks
• Experience with large-scale recommendation/ranking systems, multimodal modeling, or retrieval architectures
• Advanced degree in a related field such as machine learning, computer vision, or mathematics