Sr. Staff Machine Learning Engineer, Agentic Ads

This is a rare opportunity to lead frontier machine learning work at Pinterest on one of the company’s highest-leverage surfaces: Ads ranking and auctions. In this Sr. Staff MLE role, the right candidate will define the technical strategy for next-generation ranking systems, applying AI- and agentic-driven development loops to accelerate modeling innovation, experimentation, and automation at massive scale. They’ll partner closely with senior leaders across product, applied science, and infrastructure to shape outcomes that directly impact advertiser performance, Pinner experience, and company revenue, while also setting modeling standards, influencing architecture across the ads ML stack, and mentoring top-tier technical talent. For someone excited by deep technical ownership, broad organizational influence, and the chance to build ambitious systems using rich multimodal data and a modern ML platform, this is an exceptionally high-impact leadership opportunity.

What you’ll do:

• Lead the technical strategy for advancing ads ranking and bidding models using large scale ML and agentic development loops.
• Design, build and launch production models and signals that improve ads quality, relevance and long term value.
• Build and refine training, evaluation and feedback pipelines that continuously learn from online behavior, experimentation results and advertiser outcomes.
• Use AI to accelerate analysis and iteration while applying judgment and verification to ensure correctness and quality.
• Own the technical strategy for advancing ads ranking models, with a focus on AI/agentic development loops and automation.
• Build and refine agentic training and evaluation pipelines that continuously learn from feedback signals, offline and production behavior.
• Partner closely with product, applied science, and infra teams to translate business objectives into modeling roadmaps and success metrics.
• Ensure models meet the bar for reliability, safety, fairness, and compliance with privacy and policy constraints.
• Drive technical design reviews, set modeling best practices, and make high-impact architectural decisions across the ads ML stack.
• Mentor and uplevel other MLEs and scientists in advanced modeling techniques, tooling, and agentic workflows.
• Lead frontier work applying AI- and agentic-driven development loops to one of Pinterest’s highest-leverage systems: ads ranking and auctions.
• Shape product outcomes at massive scale, with direct impact on advertiser value, Pinner experience, and company revenue.
• Partner with senior leaders across product, research, and infra, gaining broad visibility and influence on ads strategy.
• Grow as a technical leader by setting modeling standards, mentoring senior talent, and driving multi-quarter roadmaps across teams.
• Access Pinterest’s rich multimodal data and modern ML/infra stack to experiment quickly and ship ambitious ideas.

What we’re looking for:

• Minimum of 8 years of experience in applied machine learning including large scale ranking or ads systems.
• Deep expertise in modern recommendation and ranking techniques such as gradient boosted trees and deep learning based ranking models.
• Experience building and operating ML systems at scale in languages such as Python or C++ and with modern data and experimentation platforms.
• Demonstrated ability to use AI to improve speed and quality in your day to day workflow for modeling, experimentation and analysis.
• Experience leading cross functional initiatives and influencing senior partners across product, engineering and research.
• Bachelor’s/Master’s degree in a relevant field such as computer science or statistics, or equivalent experience.
• High integrity and ownership in how you handle data, use AI responsibly and remain accountable for final decisions and deliverables.

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.
• his role will need to be in the office for in person collaboration 1–2 times/month and therefore needs to be in a commutable distance from one of the following offices: Seattle, San Francisco, Palo Alto or Los Angeles.

#LI-HYBRID
#LI-AK7