Software Engineer, Ads Measurement Products

About Pinterest

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.

The Measurement Products team helps advertisers measure the incremental impact of their Pinterest campaigns and make informed investment decisions. We own and operate core measurement products including Pinterest Conversion Lift (PCL) Studies and Brand Lift Studies. We build the platform these products run on and are evolving it into our next‑gen Measurement Hub, an extensible platform that enables faster product iteration, onboarding of new study types, and deeper integration with Pinterest’s enterprise data and experimentation systems.

Our mission is to measure true incremental impact and translate it into actionable insights for advertisers and partners.

We’re seeking a Staff Engineer to lead the architecture and technical direction of the measurement platform behind how advertisers understand the impact of their investment on Pinterest. You’ll drive end‑to‑end platform evolution (pipelines, services, correctness, reliability), partner closely with Product and Data Science, and raise the bar on rigor, trust, and operational excellence in ads measurement.

• Own end‑to‑end architecture and delivery of features for lift measurement products, from design and implementation through rollout and ongoing reliability.
• Design, build, and optimize large‑scale data pipelines that power study setup, experiment execution, and results computation.
• Drive AI enablement across the team by building the supporting tooling and processes (CI checks, test strategy, templates), coaching engineers, and ensuring adoption results in sustained improvements to delivery speed and system quality.
• Translate measurement methodology into production systems: partner with Data Science to operationalize study design requirements into pipelines, services, tooling, and guardrails.
• Build “rigor by default” mechanisms: automated data validation, randomization/holdout integrity checks, imbalance diagnostics, and continuous verification/backtesting.
• Raise the bar on data quality and trust: define SLAs, build monitoring and anomaly detection, and lead root‑cause analysis for pipeline and study issues.
• Mentor and up level engineers, driving strong technical execution and cross‑functional collaboration.

• BS+ in Computer Science (or related field) or equivalent practical experience.
• 8+ years of professional software engineering experience, with a focus on data‑intensive systems.
• Strong proficiency building and operating large‑scale data pipelines (batch and/or streaming) using Java/Scala/Kotlin or Python, plus SQL.
• Experience designing reliable services and APIs, with solid foundations in distributed systems, data modeling, and performance optimization.
• Demonstrated ability to transform a team’s engineering workflow using AI—from pilot to broad adoption—through tooling, enablement, and change leadership, with clear metrics (cycle time, defect rate, on‑call load, incident rate).
• Experience in AdTech and measurement products (e.g., conversion lift, brand lift) or adjacent experimentation/analytics platforms.
• Working knowledge of experimentation and causal measurement fundamentals (randomization, holdouts, confounders, significance, power).
• Practical experience with backtesting, controlled rollouts, and continuous verification for correctness in measurement/analytics systems.
• Demonstrated ability to lead large cross‑functional initiatives, drive alignment, and deliver measurable impact through technical leadership.

We recognize that the ideal environment for work is situational and may differ across departments.…