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Snap Inc.

Principal Machine Learning Engineer, Generative Recommendations, Level 7

Reposted An Hour Ago
Be an Early Applicant
Hybrid
5 Locations
235K-414K Annually
Expert/Leader
Hybrid
5 Locations
235K-414K Annually
Expert/Leader
Lead the vision for generative recommendations at Snap, designing and building generative models to enhance content discovery, personalization, and user engagement, while driving innovation and technical initiatives in the enterprise.
The summary above was generated by AI

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.

Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We’re deeply committed to the well-being of everyone in our global community, which is why our values are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront.

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

What you’ll do

  • 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.

Knowledge, Skills & Abilities

  • 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.

Minimum Qualifications

  • 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

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 generative 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.

Top Skills

Deep Learning
Generative Models
Machine Learning
PyTorch
TensorFlow
Transformers

Snap Inc. Palo Alto, California, USA Office

Palo Alto, CA, United States

Snap Inc. San Francisco, California, USA Office

Snap SF is nestled in SoMa, steps from the Moscone Center and a quick walk from Powell Street BART station.

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