Sr. Data Scientist, GenAI & Labeling Platforms

Role Overview

This role focuses on advancing the science and systems behind data labeling, evaluation, and Generative AI (GenAI)-enabled workflows. The position involves driving step-function improvements in data labeling capabilities by leveraging Large Language Models (LLMs), developing human-in-the-loop quality systems, and optimizing prompt/rubric design. The successful candidate will act as a senior individual contributor, executing high-impact technical work and partnering cross-functionally to turn ideas into durable platform capabilities.

Key Responsibilities

• Execute high-impact scientific work across GenAI-powered labeling and evaluation systems.
• Identify opportunities where LLMs can improve quality, speed, coverage, and cost efficiency.
• Develop prototypes demonstrating value in prompt optimization, task decomposition, quality estimation, routing, and human-in-the-loop workflows.
• Design experiments and measurement frameworks to evaluate model performance and operational tradeoffs.
• Partner with engineering, product, and data science teams to productionize successful approaches.
• Apply standards for trustworthiness (bias measurement, calibration, quality control, responsible oversight).
• Contribute to reusable methods and frameworks scalable across teams.
• Support junior scientists and contribute to the technical health of the team.

Requirements & Qualifications

• Essential Experience:
• Education/Experience: 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.
• Technical Skills: Strong hands-on experience as an individual contributor solving technically complex, high-impact data science or ML problems.
• GenAI Expertise: Experience applying LLMs or other generative AI techniques to practical workflows, systems, or products.
• Problem Solving: Ability to turn ambiguous problems into rigorous analyses, experiments, and prototypes.
• Engineering: Track record of writing high-quality code and using technical work to influence product or platform direction.
• Collaboration: Solid cross-functional collaboration skills and business/product sense to define meaningful success metrics.
• Adaptability: Self-directed learning mindset comfortable in a rapidly evolving technical landscape.

Preferred Qualifications

• Experience with labeling systems, evaluation frameworks, human judgment workflows, or internal AI tooling.

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Job Details

• Location: United States, San Francisco, CA
• Job Type: hybrid or office
• Posted: May 19, 2026