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Director, Research – Evaluation & Training
Snorkel AIManager leading a team of researchers focused on data evaluation and error analysis at Snorkel AI. Emphasizing business outcomes from research and technical analysis with a client-centric approach.
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
applied AImachine learningLLM evaluationbenchmarkingmodel behavior analysisdata valuation techniqueserror analysisdata attribution research
Soft Skills
leadershipcommunication skillsstorytellingbusiness judgmentmarket judgmenttrend analysisteam management
Tools & Technologies
Snorkelpublic benchmarkscommercial AI data products
Industry Keywords
model performancedata strategyresearch and analysiscompetitive landscapefrontier-lab landscape
About the role
Key responsibilities & impact- Own a multi-quarter roadmap centered on novel evaluation, error analysis, and data valuation techniques
- Synthesize and share trends from model-failure analysis and benchmarking into recommendations on the datasets the community should focus on and the ones Snorkel should invest in — making this team a primary input to the company's data strategy.
- Focus on data valuation techniques that quantify how Snorkel data meaningfully improves model performance
- Lead and grow a team of researchers, setting a high bar for quality, rigor and speed of execution
- Act as the primary bridge between the team's findings and Product, GTM, and our customers
Requirements
What you’ll need- 7+ years in applied AI, ML, or research roles, with 4+ years managing technical teams.
- A leader who has repeatedly turned research and analysis into business outcomes, and who instinctively connects technical findings to market and customer needs.
- Strong business and market judgment in the AI/ML space — you understand the competitive and frontier-lab landscape and can prioritize accordingly.
- Technically conversant and credible: enough depth in LLM evaluation, benchmarking, and model behavior analysis to set direction, judge experimental quality, and pressure-test results — without needing to be the deepest technical expert in the room.
- A nose for trends: able to look across many evaluation results and failure cases and extract the signal that should drive what gets built next.
- Excellent communication and storytelling skills, with the ability to make technical results legible and persuasive to non-research audiences.
- Familiarity with data valuation or data attribution research is a strong plus.
- Bonus: experience working with frontier labs, public benchmarks, or commercial AI data/eval products.
Benefits
Comp & perks- Health insurance
- Paid time off
- Flexible working arrangements
- Professional development opportunities