Staff Machine Learning Engineer

Within the Ads Intelligence team, we try to connect the dots between the goals of our advertisers and the performance of our ads delivery stack. The team plays a pivotal role in empowering advertisers and our sales team to make informed decisions regarding campaign setups, budget allocations, and ad optimizations. We are a diverse team of machine learning engineers, backend engineers, statisticians, and economists. You’ll have the opportunity to apply advanced machine learning techniques to address these challenges and maximize value for both advertisers and Pinterest.

What you’ll do:

• Budget Allocation Optimization: Develop and enhance machine learning models that optimize advertisers' budget allocations across various campaigns and channels to maximize ROI for advertisers and revenue for Pinterest.
• Campaign Recommendation Systems: Design systems that provide data-driven recommendations on campaign settings, including targeting, creatives, and bidding strategies, to boost campaign effectiveness and efficiency.
• Counterfactual Engine Development: Work with the Ads Bidding and Ads Marketplace teams to build a counterfactual engine that simulates and predicts ad performance under hypothetical scenarios for informed decision-making.
• Integration and Collaboration: Partner with cross-functional teams, including ML engineers, backend engineers, statisticians, and economists, to build robust systems that elevate ad intelligence capabilities.
• Continuous Learning and Improvement: Stay abreast of the latest developments in machine learning and advertising technologies to persistently drive innovation and improvement aligned with the team's objectives.

What we’re looking for:

• MS or PhD degree in Computer Science, Statistics or related field.
• 6+ years of industry experience, with a focus on machine learning applications in advertising.
• 2+ years of experience leading projects or teams, with a strong emphasis on collaboration and communication.
• Proficient in software engineering with a solid foundation in statistical methods.
• Hands-on experience with large-scale online e-commerce systems is a plus.
• Background in computational advertising is preferred, but not required.

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 1-2 times/month, and therefore needs to be in a commutable distance from one of the following offices: San Francisco, Palo Alto, 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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