Senior Data Scientist (GenAI & Labeling Platforms)

At Pinterest, AI isn’t just a feature, it’s a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that
To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI
Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think
You can read more about our AI interview philosophy and how we use AI in our recruiting process here
Pinterest brings millions of people the inspiration to create a life they love
Advancements in Generative AI have opened up a wealth of opportunities for improvements in productivity and labeling quality, and we’ve only scratched the surface of its capabilities
Early results show strong promise for LLM-assisted labeling — reducing time and cost, focusing 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 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 a strong senior individual contributor to execute high-impact technical work in this space, partner cross-functionally to turn successful ideas into durable platform capabilities, and grow with the team as the space evolves
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- Experience applying LLMs or other generative AI techniques to practical workflows, systems, or products
Business and product sense with the ability to define meaningful success metrics
Track record of writing high-quality code and using technical work to influence product or platform direction
Solid cross-functional collaboration skills and experience working effectively across teams
Experience with labeling systems, evaluation frameworks, human judgment workflows, or internal AI tooling is strongly preferred
Strong hands-on experience as an individual contributor solving technically complex, high-impact data science or ML problems
Ability to turn ambiguous problems into rigorous analyses, experiments, and prototypes
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
Self-directed learning mindset and comfort working in a rapidly evolving technical landscape

Job Type

Job Type: Full Time
Location: San Francisco, CA

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