The Director of Machine Learning Engineering at Pinterest will lead a diverse portfolio of Ads Quality modeling teams focused on enhancing engagement, conversion, and optimization through machine learning. This role involves shaping a unified technical strategy that connects model innovation to user value and advertiser performance. The director will oversee a team of engineering managers and machine-learning engineers, driving improvements in model quality and production outcomes. Collaboration with various teams across the organization is essential to ensure the success of machine learning initiatives.

• Minimum 12 years of experience building and deploying machine-learning systems.
• Demonstrated success leading large-scale recommendation, ranking, advertising, search, marketplace, or personalization ML teams.
• Strong understanding of modern deep-learning and recommender-system techniques.
• Experience with conversion, value, ROAS, bidding, or other lower-funnel optimization problems is strongly preferred.
• Proven ability to connect modeling objectives and offline metrics to online experiments and business outcomes.
• Experience operating production ML systems with demanding requirements for latency, availability, and reliability.
• Strong judgment in balancing near-term product delivery with foundational technical investments.
• Track record of building high-performing organizations and developing senior leaders.
• Excellent communication and collaboration skills.
• Bachelor’s degree in Computer Science, Engineering, or a related field; advanced degree preferred.

• Set the technical vision and multi-year strategy for Ads Quality machine learning.
• Lead and develop a group of engineering managers and machine-learning engineers across multiple modeling domains.
• Establish a coherent modeling roadmap across engagement, conversion, ROAS, relevance, and ranking initiatives.
• Drive improvements in model quality, calibration, generalization, and robustness across Pinterest surfaces.
• Guide the evolution of Ads models toward larger, more generalizable architectures.
• Ensure that modeling investments translate into reliable production outcomes through strong evaluation and monitoring.
• Partner closely with various teams including Ads Product, Data Science, and ML Infrastructure.
• Set expectations for data quality, privacy, reliability, and cost efficiency.
• Improve engineering velocity through better experimentation workflows and reusable modeling infrastructure.
• Build a culture of technical excellence, collaboration, and continuous learning.