Pinterest
Sr. Data Scientist, GenAI & Labeling Platforms
datafull-timeSan Francisco, CA, US; Remote, US
SALARY
Not listed
WORK TYPE
remote
JOB TYPE
full-time
INDUSTRY
ai
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About the role
About Pinterest:
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.
What you’ll do:
We are looking for an experienced and highly capable Data Scientist to help us drive step function improvements in our data labeling capabilities at Pinterest. In this role, you will:
- Execute high-impact scientific work across GenAI-powered labeling and evaluation systems
- Identify opportunities where LLMs and related methods can improve quality, speed, coverage, and cost efficiency
- Develop prototypes that demonstrate value in areas such as prompt optimization, task decomposition, quality estimation, routing, and human-in-the-loop workflows
- Design experiments and measurement frameworks to evaluate model performance, workflow outcomes, and operational tradeoffs
- Partner with engineering, product, and data science teams to productionize successful approaches
- Apply standards for trustworthiness, including bias measurement, calibration, quality control, and responsible oversight
- Contribute to reusable methods and frameworks that can scale across teams and use cases
- Support more junior scientists and contribute to the technical health of the team
What we’re looking for:
- 6+ years of combined post-graduate academic and industry experience (or PhD + 3 years) applying scientific methods to real-world problems on large-scale data
- Strong hands-on experience as an individual contributor solving technically complex, high-impact data science or ML problems
- Experience applying LLMs or other generative AI techniques to practical workflows, systems, or products
- Ability to turn ambiguous problems into rigorous
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