snorkelai · New York City, NY (Hybrid); San Francisco, CA (Hybrid); United States (Remote)
Skills: Python
About Snorkel Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! About Open Benchmarks Grants Open Benchmarks Grants is Snorkel’s $30M commitment to fund and support researchers and independent builders developing new AI evaluations. We support the teams behind Terminal-Bench, OSWorld 2.0, and Senior SWE-bench, and run a Research Fellows program for PhD students and postdocs. The program funds work in areas including physical AI, scientific discovery, cybersecurity, safety, and alignment, and helps teams test and strengthen their benchmarks before and after public release. About the Role Snorkel AI is hiring researchers to support the Open Benchmarks Red Team program. Frontier AI benchmarks can contain grading failures, exploitable shortcuts, and task errors that make results unreliable. We’re looking for a Research Scientist / Research Engineer to identify benchmark weaknesses, help develop and validate fixes, and create reusable methods to improve benchmark quality across tasks and datasets. This is a hands-on research role combining AI evaluation, adversarial testing, practical benchmark repairs, and close collaboration with benchmark authors. You'll share findings with benchmark authors, work together on fixes, and help get improved benchmark versions adopted. What You'll Do Red-team frontier benchmarks: Identify and reproduce issues such as reward hacking, grader failures, task ambiguity, shortcuts, and environment vulnerabilities. Develop benchmark repairs: Propose next steps for validated issues, implement selected…
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