Job description

Meta's Business Messaging team helps businesses meaningfully engage their customers through messaging on WhatsApp, Messenger, and beyond. Marketing messages are one of the fastest-growing parts of Meta's business: businesses use them to re-engage the customers who opted in to hear from them, through the WhatsApp Business Platform APIs, Ads Manager, and Meta's own business tools. We are looking for a Director of Engineering to lead Marketing Messages Optimization: the end-to-end delivery funnel behind every marketing message. This org owns signals, ranking, auction and pricing, and delivery infrastructure. It decides which message reaches which person, when, how often, and at what price, balancing value for businesses with a high-quality experience for people. The role combines large-scale ads ranking and auction systems with the unique constraints of a messaging surface: a limited, opted-in audience, strong privacy commitments, and a high bar for user experience. You will lead a multi-team organization of engineering managers and senior engineers, set multi-year technical strategy, and partner closely with Ads, Business Agent, WhatsApp teams as well as across several functions including data science, product, and privacy across Meta. This is a high-impact, revenue-critical role at the intersection of monetization and messaging. Lead and grow a multi-team engineering organization spanning delivery infrastructure, ranking and auction, and signals and experimentation, including hiring and developing engineering managers and senior technical leaders Set the multi-year technical vision and roadmap for marketing message delivery, including candidate retrieval, ranking, quality modeling, auction design, and pricing Drive a quality-aware auction that balances advertiser value, business outcomes, and the experience of people receiving messages Scale serving infrastructure to support rapid growth in traffic across multiple products, while improving efficiency, reliability, and observability Build the signals and measurement foundation for optimization in a privacy-constrained environment, including experimentation that cleanly separates the effects of pricing, quality, and supply Manage the funnel as one system: make tradeoffs across infrastructure capacity, model quality, and auction policy to maximize long-term value Set a high operational bar for revenue-critical systems, including on-call health, alerting, and safe launch practices during peak seasons Build strong partnerships with Ads ranking and delivery, WhatsApp, data science, product management, and privacy and policy teams to align roadmaps and land cross-organization initiatives Partner with product leadership to ensure the optimization platform serves multiple products and buying surfaces 10+ years of technical industry experience in software engineering 7+ years of engineering management experience, including managing other engineering managers Demonstrated experience in delivering large scale impact leveraging Machine Learning and partnering with cross-functional partners Track record of building and scaling engineering organizations across multiple teams or product areas Experience in establishing engineering strategy and roadmaps that span multiple teams and cross-functional partners Experience diagnosing and resolving complex organizational health challenges, including retention, team structure, and leadership development Experience driving measurable revenue impact through machine learning and infrastructure investments, validated through rigorous online experimentation Experience operating revenue-critical systems through peak traffic and spend periods Experience with notifications, messaging, or other supply-constrained surfaces where frequency, timing, and user fatigue matter Experience with large-scale candidate retrieval and serving systems, including capacity and efficiency tradeoffs Experience with auction mechanism design, reserve pricing, bidding and pacing, or quality and integrity modeling in ads systems Advanced degree in Computer Science, Machine Learning, Economics, or a related field Experience building machine learning systems under privacy constraints or limited signal availability Experience leading organizations that build large-scale ads ranking, auction, pricing, or ads delivery systems Experience setting technical direction across organizational boundaries, with key dependencies owned by partner teams

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Keywords to mirror

machine learning

Platform: meta
Effort: medium

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