Principal Machine Learning Engineer

About

We’re looking for a Principal Machine Learning Engineer to join the Generative Recommendations for Content products at Snap!

What the job involves

• Lead the vision and roadmap for generative recommendations by incorporating advanced generative models into Snap’s large-scale recommendation systems, elevating content discovery and personalization across Spotlight, Discover and Friend Stories.
• Design, build, and scale Generative modeling and build the next generation of the Ranking stack to improve discovery, personalization and user engagement across the platform.
• Develop and apply state-of-the-art multimodal generative models (text, image, video, embeddings) to:
  • Enhance user and content understanding,
  • Improve representation learning for content ranking,
  • Enable new generative recommendation experiences.
• Drive innovation across Snap’s content ecosystem by leading high-impact technical initiatives that apply generative AI to improve recommendation quality, personalization, and creator value.
• 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.
• Keep up-to-date of emerging trends and advancements in the Generative AI 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.

Requirements

• Deep understanding of generative architectures (e.g., transformers, foundational LLM or VLMs, auto-regressive decoders) 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 recommendation, personalization, or generative AI.
• 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.
• BS in technical field: such as computer science, mathematics, statistics or equivalent years of experience.
• 9+ years of post-Bachelor’s machine learning experience: or a Master’s degree in a technical field + 8+ year 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.
• (Desirable) Advanced degree in a related field.
• (Desirable) Experience with large-scale recommendation/ranking systems, multimodal modeling, or retrieval architectures.
• (Desirable) Experience with TensorFlow, PyTorch, or related deep learning frameworks.
• (Desirable) Background in integrating generative models into production pipelines.
• (Desirable) Experience partnering with cross-functional executives and management across a globally distributed organization and exercising sound judgment.
• (Desirable) Experience contributing to AI publications.