Director, Machine Learning Engineering, Ads Measurement

The role

This leadership role oversees the group that measures advertiser results and manages the quality of incoming signals at Pinterest. The director covers four sub-areas (interfaces, ingestion, user matching, and conversion quality) through a layer of people managers and tech leads. Work spans long-horizon investments in ML, data infrastructure, experimentation, privacy, and uptime, with close coordination across product, science, infrastructure, legal, and sales partners.

What you would do

• Set the engineering direction and funding priorities for the four measurement sub-areas.
• Guide people managers and senior tech leads through a multi-year program of ML and data-platform work.
• Coordinate with customer operations, sales, legal, privacy, infrastructure, ads quality, and product and science colleagues to turn commercial problems into scalable technical plans.
• Turn vague commercial questions raised by advertisers into ML and data-platform designs that can scale.
• Push the standard upward for experiment design, causal analysis, model quality, uptime, privacy, and day-to-day operations.
• Stand up forums where teams review designs jointly, agree on shared metrics, manage dependencies, and oversee launches.
• Brief executives and cross-functional partners concisely on direction, tradeoffs, risks, and results.
• Hire, keep, and grow a strong, welcoming team.

What they asked for

• Deep experience in ads measurement, identity resolution, signal systems, and large data processing.
• Prior experience running an engineering org that operates via people managers and principal-level leads.
• Proven skill setting and carrying out technical direction across several teams at once.
• Hands-on work with high-scale systems where uptime, privacy, and data quality standards are strict.
• History of raising team execution, growing leaders, and building shared ownership across people.
• Sharp analysis skills for converting murky business questions into measurable engineering results.
• Clear communication at the executive level and influence without direct authority across functions.
• Undergraduate degree in a technical discipline like CS, statistics, or ML, or equal hands-on experience.

Nice to have

• Led teams through big shifts in privacy rules, browser behavior, platform policy, or loss of signal.
• Background in ad optimization or privacy-preserving techniques.
• Familiarity with probabilistic linking, graph methods, outlier detection, event-quality modeling, or confidence estimation.
• Experience with ML infrastructure, streaming pipelines, large-scale A/B testing, and model oversight.
• Applied AI or agent-based tooling to engineering velocity, advertiser support, or data-quality work.