Director of Machine Learning Engineering

Pinterest is a visual discovery platform where hundreds of millions of people come to find inspiration and decide what to try, buy, or do next. Our Ads Quality organization builds the machine-learning systems that make ads relevant and valuable to Pinners while delivering meaningful outcomes for advertisers.

• Set the technical vision and multi-year strategy for Ads Quality machine learning, connecting model innovation to Pinner value, advertiser performance, revenue, and marketplace health.
• Lead and develop a group of engineering managers, senior technical leaders, and machine-learning engineers across multiple modeling domains.
• Establish a coherent modeling roadmap across engagement, conversion, ROAS, relevance, ranking, and foundation-model initiatives.
• Drive improvements in model quality, calibration, generalization, cold-start performance, attribution, and robustness across Pinterest surfaces.
• Guide the evolution of Ads models toward larger, more generalizable architectures, including foundation models, distillation, long-context sequence modeling, multimodal representations, and cross-domain learning.
• Ensure that modeling investments translate into reliable production outcomes through strong offline evaluation, online experimentation, launch discipline, and post-launch monitoring.
• Partner closely with Ads Product, Ads Data Science, Ads Signals, Ads Retrieval, Ads Delivery, Measurement, Core, ATG, and ML Infrastructure.
• Set expectations for training-serving parity, data quality, privacy, reliability, latency, capacity, and cost efficiency.
• Improve engineering velocity through better experimentation workflows, reusable modeling infrastructure, automation, and agentic development tools.
• Build a culture of technical excellence, candid collaboration, inclusion, ownership, and continuous learning.
• Represent Ads Quality ML in senior leadership forums and communicate strategy, tradeoffs, risks, and results clearly to technical and non-technical audiences.

• Minimum 12 years of experience building and deploying machine-learning systems, including significant experience leading managers and multi-team organizations.
• Demonstrated success leading large-scale recommendation, ranking, advertising, search, marketplace, or personalization ML teams.
• Strong understanding of modern deep-learning and recommender-system techniques, including sequence models, embeddings, multimodal models, multi-task learning, foundation models, distillation, and reinforcement learning.
• 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, calibration, privacy, reliability, and cost.
• Strong judgment in balancing near-term product delivery with foundational technical investments.
• Track record of building high-performing organizations, developing senior leaders, and creating effective operating mechanisms.
• Excellent communication and collaboration skills, with the ability to influence across organizational boundaries.
• Bachelor’s degree in Computer Science, Engineering, a related field, or equivalent experience; advanced degree preferred.

• This position is not eligible for relocation assistance.
• This role will need to be in the office for in-person collaboration 1-2x per week and therefore needs to be in a commutable distance from one of the following offices: Palo Alto, San Francisco.