Sr. Manager, Machine Learning Engineering, Ads Measurement Products

Role overview Lead a multidisciplinary engineering organization focused on building and scaling advertising measurement products that help advertisers evaluate campaign effectiveness and make confident investment decisions. The role combines people leadership, technical strategy, and cross-functional partnership to deliver rigorous, privacy-aware, and actionable measurement solutions across first- and third-party channels.

Responsibilities - Manage and grow a team of machine learning, backend, and data engineers building incrementality solutions, measurement diagnostics, anomaly detection, automated insights, and advertiser decision-support tools. - Define the multi-year technical vision and engineering roadmap, translating advertiser needs and measurement methodologies into reliable, production-grade systems. - Collaborate with Product, Data Science, Ads Engineering, and Infrastructure to productionize causal inference, experimentation, and ad-effectiveness models while maintaining methodological rigor. - Oversee development of scalable ML and data platforms supporting conversion lift, brand lift, budget-split testing, matched-market testing, marketing mix modeling, multi-touch attribution, conversion APIs, and clean-room-based measurement. - Identify high-leverage opportunities that improve the speed, scale, usability, and actionability of the broader measurement portfolio. - Hire, mentor, and develop engineering managers and senior technical talent, fostering an inclusive culture of technical excellence and accountability.

Requirements - 10+ years of industry experience building software, ML, or large-scale data systems, with 5+ years managing engineering teams responsible for production systems. - Bachelor's degree in computer science, a related field, or equivalent practical experience. - Hands-on experience with ads measurement, ad effectiveness, or incrementality, including areas such as conversion lift, brand lift, MMM, MTA, conversion APIs, or clean-room measurement. - Strong understanding of experimentation, causal inference, probabilistic modeling, or measurement using noisy, incomplete, or privacy-constrained data. - Demonstrated ability to set a technical vision, develop multi-year roadmaps, and ship scalable, production-quality systems end to end. - Proven success partnering across Engineering, Product, Data Science, Infrastructure, and business stakeholders to deliver measurable impact.

Nice to have - Experience guiding teams through trade-offs involving methodological rigor, statistical power, data quality, privacy, and time to market. - Familiarity with AI-assisted development and LLM-powered productivity tools, paired with disciplined standards for validation, explainability, and accountability.

Benefits and work setup - Hybrid arrangement with 1-2 in-person collaboration days per month; candidates must be within commutable distance of San Francisco, Palo Alto, or Seattle. - Relocation assistance is not provided for this role. - US-based applicants only; position is eligible for equity, with a published base salary range of $227,871–$469,147 USD depending on location and experience.