Principal Machine Learning Engineer, Content ML, Level 7

What you’ll do

• Lead the vision and roadmap for Snap’s large‑scale recommendation systems, elevating content discovery and personalization across Spotlight, Discover, and Friend Stories.
• Technically lead a group of talented engineers from Content ML and Platform teams to operate and scale the existing recommender system.
• Work with cross‑team ML, Infra, and Research partners to design the next‑gen recommender system and incorporate SOTA industry research in recommendation systems, foundation models, multimodal signal understanding, deep user understanding, and related areas.
• Partner with engineers, product managers, research scientists, data science, and leadership to align on ML strategy and ensure technical investments support long‑term company priorities.
• Advance the ML tech stack for recommendations, improving scalability, efficiency, reliability, and overall system performance.
• Stay up to date on emerging trends and advancements in the Rec Sys landscape and proactively identify opportunities to leverage these developments to further enhance Snap’s content capabilities.
• Advocate for and implement best practices in availability, scalability, experimentation rigor, operational excellence, and cost management.

• Deep understanding of Rec Sys architectures and experience applying them to real‑world production systems.
• Strong foundation in machine learning, deep learning, and large‑scale recommendation/ranking systems.
• 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.
• 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.
• Skilled at solving complex technical challenges, influencing architecture decisions, and driving execution across multi‑stakeholder environments.
• Strong collaboration, communication, and mentorship abilities.

• 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.
• 2+ years of experience with technical leadership or acting as the domain‑expert to a technical organization.
• Experience developing and shipping performant and scalable machine learning models for recommendation or ranking use cases.

• Advanced degree in a related field such as machine learning, computer vision, or mathematics.
• Experience with large‑scale recommendation/ranking systems, multimodal modeling, or retrieval architectures.
• Experience with Tensor Flow, PyTorch, or related deep learning frameworks.
• Background in integrating recommendation models into production pipelines.
• Experience partnering with cross‑functional executives and management across a globally distributed organization and exercising sound judgment.
• Experience contributing to AI publications.

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