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.

We are seeking a Director of Machine Learning Engineering to lead a broad portfolio of Ads Quality modeling teams focused on engagement, conversion, ROAS optimization, ranking, representation learning, and ML-powered experimentation.

In this role, you will shape and drive a unified technical strategy across Ads Quality, leading teams responsible for engagement ranking, oCPM and conversion modeling, ROAS optimization, lightweight ranking and retrieval models, foundation model adoption, sequence and multimodal modeling, and the quality and efficiency of production machine learning systems.

What you’ll do: 

• 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.

What we’re looking for:

• 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.

Relocation Statement: This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

In-Office Requirement Statement:

• We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
• 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.

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