Machine Learning Engineering Manager, Snap Inc.

• 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
• 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

Desirable

• 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
• 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