Machine Learning Engineering Manager (Advertiser Experience ML)

Join Snap as a Machine Learning Engineering Manager in the Advertiser Experience ML team. Lead a team of machine learning and software engineers to build models, systems, and data pipelines for ad review, fraud detection, and more. Set the team's technical strategy, partner with cross-functional teams, and maintain deep technical involvement in the work. Enjoy a comprehensive benefits package, including generous time off, medical coverage, and a 401(k) plan.

• Lead a team of machine learning engineers and software engineers to build models, agentic systems, and data pipelines for ad review, fraud detection, and advertiser recommendations.
• Set the team's technical strategy and own its roadmap, making prioritization calls across product and platform teams.
• Scale LLM-powered and agentic ad review to automate moderation decisions without lowering precision or policy quality.

• Track record of setting a team strategy and turning it into a roadmap that delivered business results
• Technical depth to guide design decisions across modeling, data pipelines, and inference
• Experience building production systems with large language models, including evaluation, iteration, and managing cost and latency
• Deep understanding of machine learning approaches and their application to classification, recommendation, and content understanding problems
• Management and mentorship skills
• Experience managing cross-functional stakeholders with competing priorities
• 1+ years of experience leading machine learning engineering teams
• 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
• 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 large-scale web crawling or URL and content understanding
• Experience with multimodal models across image, video, and text
• Experience with ML platform infrastructure such as distributed batch processing, workflow orchestration, and observability
• Experience with frameworks such as PyTorch or TensorFlow