Manager, Machine Learning Engineering, Advertiser Experience ML

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 operates Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world, and Specs Inc., a wholly-owned subsidiary dedicated to making computing more human, in addition to Bitmoji, Saturn, and other digital services.

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 Machine Learning Engineering Manager to join the Advertiser Experience ML team at Snap!

Advertiser Experience ML is a horizontal Machine Learning and AI team within Ads Platform that builds the intelligence layer behind advertiser-facing products and platform safety. We transform creative, web, app, and advertiser signals into production-grade ML and agentic systems that power ad moderation, fraud detection, advertiser understanding, recommendations, ranking, attribution, creative intelligence, and creator insights. Our work spans applied ML, large-scale data and inference pipelines, LLM-powered workflows, and reliable ML/AI platform infrastructure.

What you’ll do:

• Lead a team of machine learning engineers and software engineers building the models, agentic systems, and data pipelines behind ad review, fraud detection, advertiser recommendations, creative intelligence, and web and app understanding
• Set the team's technical strategy and own its roadmap, making prioritization calls across the product and platform teams that depend on its signals
• Scale LLM-powered and agentic ad review so more moderation decisions are automated without lowering precision or policy quality
• Partner with Product, Data Science, Business Integrity, Ranking, Attribution, and Recommendations teams to define success metrics, align roadmaps, and deliver measurable outcomes
• Maintain deep technical involvement in the team's work, from design and launch decisions to hands-on contributions when priorities require it
• Hire, develop, and retain a high-performing team, and hold a high bar for model quality, reliability, cost, privacy, and responsible AI

Knowledge, Skills & Abilities:

• Deep understanding of machine learning approaches and their application to classification, recommendation, and content understanding problems
• Experience building production systems with large language models, including evaluation, iteration, and managing cost and latency
• Technical depth to guide design decisions across modeling, data pipelines, and inference
• Track record of setting a team strategy and turning it into a roadmap that delivered business results
• Experience managing cross-functional stakeholders with competing priorities
• Management and mentorship skills

Minimum Qualifications:

• Bachelor’s in a related technical field such as computer science or equivalent years of experience
• 8+ years of post-Bachelor’s ML industry experience; or a Master’s degree in a technical field + 7+ year of post-grad ML experience; or a PhD in a related technical field + 4+ years of post-grad ML experience
• 1+ years of experience leading machine learning engineering teams

Preferred Qualifications:

• Experience in ads integrity, content moderation, or trust and safety
• Experience with fraud, risk, or abuse detection
• Experience with ads or marketplace recommendation systems
• Experience building agentic systems or LLM-based workflows in production
• Experience with multimodal models across image, video, and text
• Experience with large-scale web crawling or URL and content understanding
• Experience with ML platform infrastructure such as distributed batch processing, workflow orchestration, and observability
• Experience with frameworks such as PyTorch or TensorFlow

"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):

Zone B:

Zone C:

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