Research Scientist - Human-AI Systems

About the Role

Snorkel AI is looking for a Research Scientist to advance how high-quality data and environments for AI agents are created. You’ll build and optimize pipelines that combine real-world data, automated generation, and human expert input. Working with domain experts, academic partners, customers, and product and engineering teams, you’ll scale these pipelines to target frontier model performance gaps and expand data and environment diversity.

This role is ideal for someone who wants to advance frontier AI through data and environment creation and enjoys turning research into reusable, scalable systems.

Main Responsibilities

• Design, implement, and optimize reusable pipelines that combine AI capabilities with expert judgment to accelerate data and agentic environment creation.
• Design and run rigorous experiments to validate proof-of-concept approaches, measure their impact on data quality, pipeline efficiency, and model performance, and communicate findings.
• Partner with engineering, data operations, domain experts, and customers to turn research prototypes into reliable production workflows.
• Work directly with domain experts to design and test workflows that help them author, review, and refine data and environments.
• Work cross-functionally with data operations, product, and engineering to surface research findings that inform the company roadmap.
• Stay at the frontier of research in data and agentic environment creation and bring best practices into Snorkel's workflows.
• Represent Snorkel's research externally through publications, blog posts, conference talks, and customer engagements that advance the conversation around data-centric AI.

Preferred Qualifications

• Strong research background in AI, machine learning, NLP, LLMs, or related fields, with experience developing and evaluating new methods.
• Experience building environments for AI agents in automated research, computer use, coding, or professional domain workflows.
• Experience with one or more of synthetic data generation, human-in-the-loop workflows, reinforcement learning, agent environments, or model evaluation.
• Strong experimental design skills, including defining hypotheses and conducting ablations.
• Experience with software engineering best practices (e.g., clean coding, modular design, version control).
• Ability to collaborate with domain experts and translate their knowledge into concrete tasks, evaluation criteria, and repeatable workflows.
• Comfort with rapid iteration, ambiguous research questions, and moving ideas from experimentation into production.
• Ph.D. in machine learning, NLP, or a related field preferred; equivalent industry or research lab experience considered.