Director of Machine Learning Engineering (Ads Quality)

Pinterest; San Francisco (CA); On-site; USD 260,000 - 520,000; Full time

Job summary

Pinterest seeks 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. You will shape a unified technical strategy across Ads Quality and guide a team of managers and engineers toward scalable, robust production ML systems.

As a senior leader, you will drive a multi-year roadmap spanning engagement, conversion, ROAS, relevance, and

Qualifications

• 12+ years of experience building and deploying ML systems and leading managers/multi-team organizations.
• Ability to connect modeling objectives and offline metrics to online experiments and business outcomes.
• Experience with conversion, ROAS, bidding, or other lower-funnel optimization preferred.
• Proven success leading large-scale recommendation, ranking, advertising, or personalization ML teams.
• Strong understanding of modern deep learning and recommender-system techniques.

Responsibilities

• Set the technical vision and multi-year strategy for Ads Quality ML.
• Lead and develop engineering managers, senior leaders, and ML engineers.
• Create a coherent modeling roadmap across engagement, ranking, ROAS, and foundation-model initiatives.
• Improve model quality, calibration, generalization, and reliability in production systems.
• Guide evolution toward larger architectures, foundation models, and multimodal learning.
• Ensure production outcomes via offline evaluation, online experiments, and disciplined launches.
• Collaborate with Ads Product, Data Science, Signals, Retrieval, Delivery, Measurement, Core, ATG and ML Infra.

Skills

People leadership

ML systems

Recommendation models

Online experimentation

Communication

Production latency

Strategic thinking

Cross-functional collaboration

Education

Bachelor's degree in CS/Engineering or related

Advanced degree preferred

Job description

• 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
• 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

Requirements

• Minimum 12 years of experience building and deploying machine-learning systems, including significant experience leading managers and multi-team organizations
• Proven ability to connect modeling objectives and offline metrics to online experiments and business outcomes
• Experience with conversion, value, ROAS, bidding, or other lower-funnel optimization problems is strongly preferred
• 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
• Bachelor's degree in Computer Science, Engineering, a related field, or equivalent experience; advanced degree preferred
• Experience operating production ML systems with demanding requirements for latency, availability, calibration, privacy, reliability, and cost
• Excellent communication and collaboration skills, with the ability to influence across organizational boundaries
• Track record of building high-performing organizations, developing senior leaders, and creating effective operating mechanisms
• Strong judgment in balancing near-term product delivery with foundational technical investments