Director, ML Engineering for Ads Quality

Palo Alto (CA)

Hybrid

USD 315,000 - 551,000

Full time

2 days ago

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Job summary

Pinterest is seeking a Director of Machine Learning Engineering to lead a broad portfolio of Ads Quality modeling teams. You will shape a unified technical strategy across Ads Quality and drive improvements in engagement, conversion, ROAS, and ranking.

You will manage engineering managers and ML engineers, build scalable infrastructure, and communicate strategy to senior leadership while ensuring production systems meet latency, privacy, and cost targets.

Qualifications

12+ years of experience building ML systems and leading managers
Experience 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

Responsibilities

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