Sr. Technical Program Manager, Ads Performance

Pinterest•San Francisco, CA

1d•Remote

About The Position

The Ads Performance organization is responsible for maximizing advertiser performance and revenue outcomes through Pinterest’s Ads Delivery and Ads Modeling systems. This team drives high-impact, cross-functional work that improves the efficiency, relevance, and scalability of Pinterest’s ads stack, helping advertisers achieve measurable results while supporting strong user experiences.

Requirements

5+ years of experience as a Technical Program Manager or in a related role.
Strong leadership skills with an entrepreneurial mindset and the ability to lead new initiatives from the ground up in ambiguous environments.
Passion for execution and a track record of driving large, complex programs to completion.
Solid technical background with the ability to understand system design, technical challenges, and risks, and to break down complex problems to drive decisions.
Strong analytical skills, including experience using metrics and data to identify opportunities, risks, and incidents.
Excellent written and verbal communication skills, with the ability to explain nuanced technical concepts to a range of internal and external stakeholders.
Proven ability to influence teams and drive alignment across multiple stakeholders and organizations.
Experience building scalable processes and operating mechanisms in cross-functional environments.
Demonstrated AI-first execution mindset, including the ability to use GenAI to accelerate planning, communications, workflows, and decision-making, while applying strong judgment and operating within governance expectations.

Nice To Haves

Experience in the ads space, especially across ads ecosystems, measurement, APIs, data pipelines, integrations, or data products.
Understanding of user privacy is a plus.

Responsibilities

Own end-to-end program delivery for complex Ads Performance initiatives, driving planning, execution, and launch across multiple teams with clear goals, milestones, and success metrics aligned to monetization outcomes and company OKRs.
Turn ambiguous problem spaces into clear program charters, roadmaps, and decision points, while proactively identifying and resolving gaps in ownership, requirements, resourcing, and data readiness.
Partner closely with Engineering, Product, Data Science, Design, Analytics, Sales, Business Development, Research, and Product Marketing to align stakeholders and turn opportunities into actionable outcomes.
Drive development of new performance capabilities across ads delivery and modeling, including initiatives related to bidding, ranking, optimization, and other strategies that improve ROI and user experience.
Help scale existing systems to support new surfaces, formats, markets, and use cases while maintaining reliability, latency, and cost efficiency.
Influence technical direction and tradeoff decisions by facilitating alignment on architecture, surfacing risks and uncertainties early, and helping teams navigate decisions across accuracy, latency, experimentation rigor, privacy, and measurement needs.
Build durable program mechanisms and best practices, including intake, prioritization, RAID tracking, decision logs, launch criteria, and post-launch learning loops.
Communicate program state, risks, and recommendations clearly to cross-functional stakeholders and leadership, and proactively escalate when broader alignment or decisions are needed.
Use GenAI as the default operating model for EP PgM execution—producing AI-assisted first drafts of core program artifacts, modernizing high-toil workflows into AI-first mechanisms (e.g., intake triage, status synthesis, action/decision extraction, risk & dependency tracking), and synthesizing signals to proactively surface risks, decision/trade-offs, and escalation paths.
Prototype solutions to augment decisions through data (e.g. dashboards, data analysis) or simplify processes (e.g. process and workflow helpers, or internal tools) using AI coding assistants (“vibe coding”).
Follow Pinterest AI guidance for risk, governance, and safety-by-design: appropriately handle sensitive data, validate AI-generated outputs, document assumptions/limits, and ensure AI-assisted workflows meet applicable policy/compliance expectations before broad adoption.