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Salary: USD 200,000 - 375,000 / annual
Snorkel AI is seeking a Research Scientist to advance how high-quality data and environments for AI agents are created. The company's mission is to help enterprises transform expert knowledge into specialized AI at scale, with a focus on data-centric AI development.
In this role, you will design, implement, and optimize reusable pipelines that combine AI capabilities with expert judgment to accelerate data and agentic environment creation. You'll conduct rigorous experiments to validate proof-of-concept approaches, measure their impact on data quality, pipeline efficiency, and model performance, and communicate findings to stakeholders.
Key responsibilities include:
- Designing and running rigorous experiments to validate approaches and measure impact on data quality, pipeline efficiency, and model performance
- Partnering with engineering, data operations, domain experts, and customers to turn research prototypes into reliable production workflows
- Working directly with domain experts to design and test workflows that help them author, review, and refine data and environments
- Collaborating cross-functionally with data operations, product, and engineering to surface research findings that inform the company roadmap
- Staying at the frontier of research in data and agentic environment creation and bringing best practices into Snorkel's workflows
- Representing Snorkel's research externally through publications, blog posts, conference talks, and customer engagements
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.
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 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 (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
