Sr. Engineering Manager, Programmatic Ad Tech, tvScientific

Pinterest is on a mission to bring everyone the inspiration to create a life they love, and they are seeking a Sr. Engineering Manager for their Programmatic Ad Tech team at tvScientific. This role involves leading architectural decisions for the ad server tech stack, overseeing subteams, and driving the ad server independence initiative.

Responsibilities:

• Lead and make key architectural decisions across the tvScientific ad server tech stack and its major integration points
• Take technical ownership of the ad server independence roadmap, including developing a clear understanding of the current state of initiatives, ownership across subcomponents, and resourcing needs
• Oversee and organize the subteams supporting the initiative, including bidder, index building, and VAST serving
• Serve as the technical DRI for bidder integration work with Pinterest, partnering closely on milestone sequencing, delivery planning, and execution
• Identify and drive opportunities for shared components and common infrastructure with Pinterest where it makes strategic and technical sense
• Participate in key strategic technical roadmap and architecture decisions for the Ad Server team
• Help scale the team over time, including supporting the growth of a multidisciplinary engineering organization

Requirements:

• Bachelor's degree in Computer Science, Engineering, a related field, or equivalent practical experience
• 9+ years of software engineering experience, including 5+ years of ad industry experience with 3+ years deep in programmatic ad tech
• 5+ years of engineering management experience
• A history of designing scalable, resilient, and observable production systems
• Demonstrated ability to build and operate high volume, data-intensive services
• Experience working with ML in low-latency environments
• Strong data literacy and the ability to use data to inform technical and strategic decisions
• Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs
• Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review)
• High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables