Senior/Staff Research Scientist (Frontier Benchmarks)

Requirements

• Strong research background in AI/ML evaluation, NLP, or related fields, with a track record of rigorous experimental design — especially around measuring the impact of training and evaluation data on model behavior,
• Exceptional communication skills — able to present complex technical findings clearly to both technical and non-technical audiences,
• Comfort operating in a fast-moving, cross-functional environment with ambiguous problem spaces,
• Genuine interest in GTM strategy, startup dynamics, and the commercial side of AI data services,
• Ph.D. in machine learning, NLP, or a related field preferred; equivalent industry or research lab experience considered

What the job involves

• We're looking for a Staff or Senior Research Scientist to collaborate with partners and lead the development of the next frontier benchmarks and datasets,
• This is a highly visible, customer-facing role at the intersection of research, company strategy, and go-to-market,
• You'll design datasets taking into account frontier model performance and work with our academic partners, and then partner with delivery, product and go-to-market to scale out production,
• You will also serve as a credible technical partner for our customers, prospects, and drive results that impact the broader research community,
• This role reports directly to the Head of Research and is ideal for someone who is energized by cross-functional work and wants to understand how startups operate across research, data operations, and commercial teams,
• Design state of the art datasets that drive frontier model training and evaluation based on current model performance and academic partnerships,
• Translate benchmark insights into clear, compelling narratives that articulate the ROI of expert-curated data for customer-facing presentations, technical reports, and go-to-market materials,
• Work cross-functionally with data operations, product, engineering, and strategy to surface research findings that inform the company roadmap,
• Stay at the frontier of LLM evaluation research 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

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