About the company
Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.
Responsibilities
- Design target ROAS and automatic bidding & pacing algorithms for ads.
- Explore bid landscapes to maximize revenue and advertiser value.
- Work with product and sales teams to design and implement bidding strategies for new ad products.
- A/B Experiment with bidding algorithm improvement ideas.
- Collaborate with other teams to identify bidding and budgeting opportunities and capitalize on them.
Requirements
- Bachelor's degree in Computer Science, Statistics, a related field, or equivalent experience.
- 6+ years of industry experience in ads delivery, automatic bidding / pacing, target ROAS bidding, ads marketplace / utility, or machine learning.
- Proven Staff-level technical leadership as an IC: driving technical direction and cross-team alignment without formal people management.
- Demonstrated ability to use AI to improve speed and quality of your workflow, with a strong track record of validating and stress-testing AI-assisted outputs.
- Degree in Computer Science, Statistics, or a related field.
- Direct experience specifically with bidding or pacing is a big plus.
- 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.
Conditions
- This position is not eligible for relocation assistance.
- This role will need to be in the office for in-person collaboration 3 times per quarter and therefore needs to be in a commutable distance from one of the following offices: San Francisco, Palo Alto, Seattle.
- The position is also eligible for equity.
- Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.