Life Sciences Research Assistant for AI Benchmark Development
Overview
Remote Life Sciences Research Assistant helping build next generation AI benchmarks. You’ll design authentic evaluation tasks in biology, synthesize sources like raw data and lab protocols, and define clear methods and interpretations for each task. You’ll create multi-item rubrics that assess rigor, data interpretation, and source fidelity, ensuring the work reflects real-world scientific nuance. Collaboration with project coordinators happens asynchronously to refine deliverables. Bachelor’s or Master’s in biology or related field and hands-on lab experience are preferred.
What You'll Do6
- 1Develop complex, authentic life sciences evaluation tasks such as experimental design critique, protocol analysis, and primary data interpretation
- 2Source and summarize research materials including raw data, lab protocols, technical literature, and related documents
- 3Document the correct methodological approach and biological interpretation for each task
- 4Create comprehensive rubrics that measure methodological rigor, data interpretation, and source fidelity for AI evaluation tasks
- 5Ensure tasks capture real-world scientific nuance, including ambiguity and depth, not simplified scenarios
- 6Coordinate asynchronously with project coordinators to incorporate feedback and improve deliverables
Requirements7
- 1Bachelor's or Master's degree in biology, biomedical sciences, biochemistry, or a closely related field
- 2At least 2 years of hands-on research or lab experience with primary data analysis and scientific protocols
- 3Proven ability to manage protocol-driven, multi-source workflows
- 4Strong scientific reasoning, source synthesis, and evaluation of methodologies
- 5Excellent written English with experience drafting or reviewing scientific documents
- 6Experience handling diverse data formats (CSV, spreadsheets, PDFs, technical docs)
- 7Attention to detail and commitment to rubric fidelity to produce high-quality tasks
Who Should Apply
The ideal candidate has formal biology training and a track record of direct data analysis and protocol use. They can design tasks that reflect real lab work and document their reasoning clearly. This role may not be suitable for someone without hands-on research experience or without experience synthesizing multiple sources. Common fit criteria gaps include limited lab experience, weak documentation skills, or difficulty translating complex methods into evaluative tasks. Expect candidates to be judged on the quality of task design, source synthesis, and rubric clarity.
Salary Insight
Pay is per completed task and published pay ranges are not provided here; compensation is described as output-based and determined by task completion and project specs.
Location
Required Skills
Application Tip
Highlight a concrete example of a multi-source task you designed or analyzed, including data interpretation and rubric criteria, and quantify the outcome or quality improvements.
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