Remote Scientific Research

Aston Carter•New York, NY

8d•$60 - $60•Remote

About The Position

This is a remote contract position for a Scientific Researcher. The role involves reviewing and evaluating AI-generated responses to life-sciences prompts for scientific accuracy, clarity, and appropriateness. Responsibilities include annotating and ranking model outputs to guide AI training, writing clear, expert-level answers to scientific questions, and flagging errors or misconceptions. The researcher will also propose, design, and build realistic tasks drawn from their own research, run these tasks against AI models, and iterate based on feedback. Precision in following instructions, maintaining quality and throughput, and working independently in a remote, self-directed environment are key. The role requires a hands-on practitioner with strong written communication skills and the ability to quickly understand complex data packages, even outside their specialization. Baseline tech literacy and familiarity with LLM use in life sciences are also necessary. The engagement is for 30-40+ hours per week for a full 12-week period.

Requirements

Master’s or PhD (or current PhD candidate) in Biology or a directly related field
3+ years of hands-on experience in drug discovery, computational biology/bioinformatics, or experimental biology.
Strong written communication skills; ability to explain complex concepts clearly and concisely.
Comfortable working independently in a remote, self-directed environment.
Can ramp quickly on unfamiliar work when provided written material or instructions.
Hands-on practitioner: you currently do (or recently did) this work at a detailed, individual-contributor level — not solely in a managerial capacity.
Careful reader of written instructions; receptive to feedback and revision cycles.
Can orient in a company data package of 100MB to 30GB+ and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization.
Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing).
Available to commit 30–40+ hours per week for the full 12-week engagement.
A general understanding of LLM use and the AI landscape.
You’ve used LLMs (like ChatGPT or Claude) in life sciences professional work, and have judgment to tell a well-reasoned answer from a plausible-sounding but incorrect one.

Nice To Haves

Biology
Oncology
drug discovery
computational biology
experimental biology
artificial intelligence
data entry
Biochemistry
Research & development
Molecular biology
Data analysis
Chemistry
Cell biology

Responsibilities

Review and evaluate AI-generated responses to life-sciences prompts for scientific accuracy, clarity, and appropriateness.
Annotate and rank model outputs to guide AI training and improvement.
Write clear, expert-level answers to scientific questions that serve as model training examples.
Flag errors, misconceptions, or ambiguous outputs and provide corrective guidance.
Propose, design, and build realistic tasks drawn from your own research — including the scenario and prompt, realistic supporting input files, and a detailed rubric for grading AI performance.
Run your tasks against AI models and iterate based on expert reviewer feedback.
Follow detailed written instructions precisely, including document and upload format requirements.
Maintain consistent quality and throughput throughout the sprint engagement.