Life Sciences Research Assistant

About this role

Role Title: Life Sciences Research Assistant Role Type: Contractor Location: Remote micro1 is engaging Life Sciences Research Assistants to contribute to a customer's project focused on creating next-generation AI benchmarks. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.

You'll design and author expert-level evaluation tasks, using authentic research artifacts and technical challenges that mirror advanced practice in the life sciences. Each assignment will test AI agents with realistic data sets, multi-step problem-solving, and rigorous evaluation rubrics that go beyond textbook science. Scope of Work Develop complex, authentic research evaluation tasks in the life sciences domain (e.g., experimental design critique, protocol analysis, and primary data interpretation).

Source and synthesize research materials, including raw experimental data, lab protocols, technical literature, and supplementary documents. Define and document the correct methodological approach and biological interpretation for each task. Create comprehensive, multiple item rubrics targeting methodological rigor, data interpretation, and source fidelity for LLM-based evaluation. Ensure all tasks reflect the nuance, ambiguity, and depth of real-world scientific work — not simplified or hypothetical scenarios.

Collaborate asynchronously with project coordinators for feedback and refinement of deliverables. Preferred Qualifications Bachelor's or Master's in biology, biomedical sciences, biochemistry, or a closely related discipline. Minimum 2 years of hands-on research or laboratory experience with primary data analysis and scientific protocols. Demonstrated expertise in protocol-driven, multi-source scientific workflows. Mastery of scientific reasoning, source synthesis, and methodological evaluation.

Strong written English skills, with experience drafting or reviewing scientific documentation. Familiarity with handling diverse data formats (CSVs, spreadsheets, PDFs, technical documents). Attention to detail and commitment to rubric fidelity, ensuring high-quality, objective task design. Compensation Structure Compensation is output-based; experts are paid per task that meets the project specifications. The time required to complete work may vary depending on the expert’s experience and workflow.