Director, Machine Learning Engineering, Ads Quality

The role

This director-level position oversees several modeling teams within Pinterest's Ads Quality organization, which develops the machine-learning systems that decide which ads reach users and how well they perform for advertisers. The leader sets a unified technical strategy spanning engagement, conversion, ranking, and foundation-model work, and coordinates closely with ads product, data science, infrastructure, and delivery groups.

What you would do

• Define the multi-year technical direction for ads machine learning and tie model progress to user value, advertiser results, and revenue
• Manage and grow a mix of engineering managers, senior technical contributors, and ML engineers spread across several modeling areas
• Map out a sequenced plan that links engagement, purchase-intent, return-on-spend, relevance, ranking, and foundation-model efforts
• Raise model accuracy, calibration, and resilience, especially for new ads and attribution, across all Pinterest placements
• Steer the move toward larger, more transferable architectures such as foundation models, compressed student models, and multimodal encoders
• Make sure research gains reach production through disciplined offline testing, live experiments, and monitoring after release
• Collaborate with ads product, data science, signals, retrieval, delivery, measurement, and infrastructure partners
• Present strategy, trade-offs, and outcomes to senior leadership and to both technical and non-technical stakeholders

What they asked for

• At least twelve years building and shipping machine-learning systems, including substantial time leading people managers and multi-team groups
• Track record leading large recommendation, ranking, ads, search, marketplace, or personalization ML organizations
• Solid working knowledge of modern deep learning and recommender techniques such as sequence models, embeddings, multi-task learning, and reinforcement learning
• Ability to link offline metrics and modeling goals to online experiment results and business impact
• Hands-on experience running production ML systems with strict latency, uptime, calibration, privacy, and cost constraints
• Good judgment balancing short-term shipping with longer-term foundational investment
• History of developing senior leaders and building effective team operating routines
• Strong communication and influence across organizational lines
• Bachelor's degree in computer science, engineering, or a related field, or equivalent experience

Nice to have

• Prior work on lower-funnel optimization such as conversion, value, bidding, or ROAS problems
• Advanced degree