Staff Machine Learning Engineer, Ads Measurement Products

We’re looking for a Staff Machine Learning Engineer to lead the development of ML systems that power Pinterest’s first- and third-party ads measurement products. In this role, you’ll set the technical direction for scalable, trustworthy, and privacy-aware ML solutions that help advertisers understand the impact of their investment on Pinterest. You’ll work across Product, Engineering, Data Science, and external partners to turn rigorous measurement methods into production systems that improve measurement quality, efficiency, and decision-making.

What you’ll do

• Lead the design, implementation, and productionization of ML-powered components for ads measurement products, including areas such as measurement methodologies, diagnostics, anomaly detection, automated insight generation, and advertiser decision-support.
• Build and evolve scalable ML and data pipelines that support first- and third-party measurement products, partnering with infrastructure and product engineering teams to create reliable, maintainable, and performant systems.
• Partner closely with Data Science to translate causal inference, incrementality, and experimentation methodologies into production-grade systems and tools that increase the speed, scale, and usability of measurement products without compromising rigor.
• Collaborate with internal and external measurement partners, such as clean rooms, conversion APIs, MMM partners, and MTA vendors, to integrate high-quality signals and develop joint measurement solutions.
• Establish ML engineering best practices across data quality, feature pipelines, evaluation, experimentation, monitoring, and model governance within Measurement Products, and mentor engineers and partner teams working on ML-powered components.
• Influence the Ads Product and Engineering roadmap by identifying high-leverage opportunities to apply ML to measurement workflows and products, and by driving clear technical trade-offs, interfaces, and success metrics across teams.
• Use AI to accelerate development, prototyping, analysis, and iteration, while applying strong judgment, testing, and verification to ensure correctness, explainability, data protection, and advertiser trust.

What we’re looking for

• 7+ years of experience building and deploying large-scale ML systems in production, ideally in ads, measurement, recommendation, ranking, search, or closely related domains.
• Degree in Computer Science, Statistics, Engineering, or a related technical field, or equivalent experience.
• Meaningful hands-on experience in ads measurement, ad effectiveness, or incrementality domains, such as conversion lift, brand lift, budget-split testing, matched-market tests, MMM, MTA, conversion APIs, or clean-room-based measurement.
• Strong end-to-end ML ownership as an individual contributor, including scoping ambiguous problems, designing labels and features, building training and inference workflows, and defining robust offline and online evaluation strategies.
• Solid software engineering skills in at least one modern programming language such as Python or Java, plus strong experience with SQL and large-scale data systems.
• Expertise in probabilistic modeling, experimentation, and measurement under noisy or partial labels, with the ability to design trustworthy metrics and make principled trade-offs across precision, recall, bias, power, coverage, and data quality.
• Proven Staff-level technical leadership as a hands-on IC, including setting technical direction, driving multi-quarter roadmaps, and aligning senior stakeholders across Product, Engineering, Data Science, Infra, and partner teams.
• Demonstrated experience using AI coding assistants and LLM-powered productivity tools to improve speed and quality in development, experimentation, and data exploration, with a clear approach to validation, critical review, and accountability for final outputs.

In-Office Requirement Statement:

• We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
• This role will need to be in the office for in-person collaboration 1 time per week and therefore needs to be in a commutable distance from one of the following offices San Francisco, Palo Alto, or Seattle. 
  
  

Relocation Statement:

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

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