Pinterest is seeking an Engineering Manager to lead the Ads Serving Platform team, which is responsible for delivering relevant ads reliably and efficiently across Pinterest surfaces. The role focuses on managing the engineering team and platform to ensure high performance, reliability, and compliance throughout the ad-request lifecycle. The manager will work closely with Ads and Core engineering teams to modernize frameworks and enhance product iteration. This position emphasizes the use of AI to improve engineering workflows and requires strong leadership and collaboration skills.

• Proven experience managing software engineering teams and building high-performance backend or infrastructure platforms.
• Demonstrated success in hiring, performance management, career development, and fostering inclusive team environments.
• Strong expertise in distributed systems, online serving, low-latency services, or infrastructure platforms.
• Demonstrated ability to make sound tradeoffs across latency, reliability, scalability, cost, and developer experience.
• Strong communication and collaboration skills with technical and cross-functional partners.
• Proficiency in one or more programming languages such as Java, C++, Go, or Python.
• Track record of meeting strict serving latency SLAs, operational targets, and optimizing cost-per-impression at scale.
• Demonstrated ability to use AI to improve the speed and quality of day-to-day engineering workflows.
• Bachelor’s degree in Computer Science, a related technical field, or equivalent experience.

• Build and lead a team of high-caliber software engineers.
• Lead engineering team and technical strategy for high-throughput, low-latency ad serving platform.
• Ensure platform reliability, high availability, and operational excellence across the end-to-end ad-request lifecycle.
• Drive infrastructure performance, cost efficiency, and compliance controls while supporting business growth.
• Improve developer productivity and platform flexibility for product and ML feature integrations.
• Use AI to accelerate analysis, debugging, design exploration, and iteration while applying sound engineering judgment.
• Raise the quality bar through design reviews, code reviews, documentation, and operational excellence.
• Mentor engineers and scale technical leadership across the organization.