Sr. Staff Data & Applied Scientist, GenAI & Labeling Platforms

Pinterest brings millions of people the inspiration to create a life they love. To evolve this mission our product and engineering teams need to innovate. Advancements in Generative AI have opened up a wealth of opportunities for improvements in productivity, and we've only scratched the surface of its capabilities in the prompting and labeling space. Early results show strong promise for the role it can play in these tasks, reducing the time and cost of our labeling efforts, focusing our surveys and human rater efforts on higher value problems, and improving the accuracy of our learnings.

This role focuses on advancing the science and systems behind labeling, evaluation, and GenAI-enabled workflows. The work spans areas such as LLM-assisted labeling, human-in-the-loop quality systems, prompt and rubric design, model evaluation, and methods for improving the speed, consistency, and usefulness of judgment-based data.

We're looking for an accomplished senior individual contributor to lead high-impact technical work in this space. This person will drive foundational innovations, prototype new capabilities, establish rigorous evaluation approaches, and partner cross-functionally to turn successful ideas into durable platform capabilities.

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:

• Drive 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 rigorous measurement frameworks to evaluate model performance, workflow outcomes, and operational tradeoffs
• Partner with engineering, product, and data science teams to shape roadmaps and productionize successful approaches
• Establish strong standards for trustworthiness, including bias measurement, calibration, quality control, and responsible oversight
• Create reusable methods and frameworks that can scale across teams and use cases
• Mentor other scientists and contribute technical leadership across the organization
  
  

What we're looking for:

• 10+ years of combined post-graduate academic and industry experience applying scientific methods to solve real-world problems on large-scale data
• Deep hands-on experience as an individual contributor solving technically complex, high-impact data science or ML problems
• Strong experience applying LLMs or other generative AI techniques to practical workflows, systems, or products
• Demonstrated ability to turn ambiguous problems into rigorous analyses, experiments, prototypes, and scalable solutions
• Proven track record of writing high-quality code and using technical work to influence product or platform direction
• Strong cross-functional collaboration skills and a track record of influencing leaders and peers through data and technical judgment
• Strong business and product sense with the ability to define meaningful success metrics and prioritize high-value opportunities
• Self-directed learning mindset and comfort working in a rapidly evolving technical landscape
• Experience with labeling systems, evaluation frameworks, human judgment workflows, or internal AI tooling is strongly preferred

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/quarter and therefore can be situated anywhere in the country.
  
  

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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