We are the Feature Infra team, part of the broader Ads AI Infrastructure team, which is responsible for all of Meta Ads Revenue (98% of all Meta Revenue). Our organization has been specifically formed to address one of the biggest challenges facing Meta Ads today - building a highly reliable, scalable, efficient feature infrastructure that powers ads delivery, while enabling rapid ad product and ML innovations that accelerate the growth of Meta's Ad business. We are a lean team, but carry a big responsibility with significant opportunities to deliver high impact to both the topline and bottom line of Meta.
Responsibilities
Lead the team to build the next-gen Feature Infrastructure with high reliability, scalability, efficiency, and dev velocity, unlocking rapid product and machine learning innovation for ads delivery
• Collaborate with cross-functional teams to drive technical innovation and proven ad product experience
• Work on both 0-1 as well as mission-critical scaled systems in the Feature Infrastructure stack
• Build a high-performing engineering team while fostering a work environment of continuous learning, growth, and improvement
Minimum Qualifications
• Experience leading engineering teams that own high-traffic, low-latency large-scale online infrastructures
• 2+ years of experience managing managers in engineering organizations, 5+ years of experience managing technical engineering teams
• B.S. or M.S. in Computer Science, Engineering, or a related technical discipline, or equivalent experience
Preferred Qualifications
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Track record of driving cross-functional technical initiatives across multiple teams
• Experience with feature platforms, feature stores, or ads infrastructure systems
• Experience building and scaling distributed systems handling millions of queries per second
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience with machine learning infrastructure or ML-powered product development
• Experience mentoring and developing engineering managers
