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Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together. The Company's three core products are Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world; Lens Studio, an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, Spectacles.

We're looking for a Principal Machine Learning Engineer to join the Content ML team at Snap! We build large-scale recommender systems for all of Snap's video content products.

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. We actively participate in and publish at top-tier conferences.

• 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 RecSys 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.

Knowledge, Skills & Abilities

• Deep understanding of RecSys 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.

Minimum Qualifications

• 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

Preferred Qualifications

• 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 TensorFlow, 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

If you have a disability or special need that requires accommodation, please don't be shy and provide us some information.

"Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a "default together" approach and expect our team members to work in an office 4+ days per week.

At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets.

We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable).

Our Benefits: Snap Inc. is its own community, so we've got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap's long-term success!

Compensation

In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future.

Zone A (CA, WA, NYC):

The base salary range for this position is $276,000-$414,000 annually.

Zone B:

The base salary range for this position is $262,000-$393,000 annually.

Zone C:

The base salary range for this position is $235,000-$352,000 annually.This position is eligible for equity in the form of RSUs.