Software Engineering Manager - Feature Infra

Job description

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

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
• 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