Staff Machine Learning Engineer - Ads Conversion Core Modeling

We are looking for a Staff Machine Learning Engineer to lead the technical vision for our Ads Conversion Core Modeling team, building the state-of-the-art systems that power our global marketplace.

What you’ll do: 

• Lead the technical direction and development of state-of-the-art applied ML projects for ads conversion.  
• Design and build large-scale DNN models to improve user action prediction with low latency.  
• Mine text, visual, and user signals to better understand intention and infer interests from online activity.  
• Use AI to accelerate analysis and iteration, while applying judgment and verification to ensure correctness and quality.  
• Automate repeatable tasks such as documentation, reporting, and QA checks to speed up the development lifecycle.  
• Coach and mentor engineers while collaborating with product and sales to design new ad products.

What we’re looking for:

• Bachelor's degree in Computer Science, Statistics, or a related field.
• Minimum 6 years of industry experience building production ML systems at scale (Search, Recommendations, or Ranking).  
• Minimum 2 years of experience leading technical projects or teams.  
• Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs.  
• Experience with Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring.
• Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration.
• High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final deliverables.
• Strong mathematical foundation and experience with statistical methods and A/B testing. 

Relocation Statement: 

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

In-Office Requirement Statement:

• We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
• This role will need to be in the office for in-person collaboration 1-2 times per month and therefore needs to be in a commutable distance from one of the following offices: San Francisco, Palo Alto, Seattle.

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