$50–80/hr
The role in one line
A clinical publications author who evaluates AI-generated medical manuscripts and scientific content.
Written by Training Turk from the public listing; it may be incomplete or out of date. Read the full posting on micro1.
What you would do
- Author realistic evaluation tasks based on actual peer-reviewed journal manuscripts, conference presentations, abstracts, and scientific posters
- Assess whether AI-generated manuscript sections comply with journal formatting, publication guidelines (ICMJE, GPP, CONSORT, STROBE), and international ethics standards
- Evaluate scientific accuracy by interpreting clinical data from source documents and identifying genuine errors in medical reasoning or data representation
- Verify publication claims by tracing statements to original clinical study reports, protocols, statistical analyses, and figure/table data
- Provide detailed structured feedback on each assessment to enable precise AI model improvement and learning
Who they are looking for
- Minimum 5 years dedicated medical or scientific publication authoring experience in sponsor, CRO, MedComms agency, or consulting roles
- Hands-on background developing or editing peer-reviewed articles in respected biomedical and clinical journals
- Proficiency with publishing standards: ICMJE, GPP3/GPP2022, CONSORT protocols, copyright rules, and submission processes
- Demonstrated capability to interpret complex clinical data including tables, figures, listings, and statistical analysis plans
- Outstanding written communication ability and demonstrated skill in delivering targeted, actionable feedback on scientific manuscripts
Skills this role asks for
What the interview is likely to probe
1.Publication guideline compliance
Manuscripts must meet journal and ethics standards or face rejection; interviewer assesses whether you understand the landscape of guidelines and can evaluate compliance rigorously.
Expect something like: “AI abstract for oncology trial omits primary endpoint until results section. This violates structured abstract requirements. How would you evaluate this, reference standards, and provide feedback?”
2.Clinical data tracing and verification
Misrepresenting data has real consequences; interviewer checks whether you would catch AI errors by actually reading source data and spot claims that don't match evidence.
Expect something like: “AI writes 'response rates improved 25% from baseline.' Clinical study report shows 40% baseline, 52% at week 12. Is this right, and how would you verify claims match source data?”
3.Scientific accuracy vs. stylistic preference
Good feedback distinguishes errors from preferences; interviewer evaluates whether you can identify genuine scientific problems versus cases where multiple interpretations are valid.
Expect something like: “AI describes results as 'clinically significant benefit' using p-value 0.048, but pre-specified threshold used effect size. Is this error or style, and how would you provide feedback?”
4.Structured feedback for AI improvement
Vague feedback doesn't help AI models learn; interviewer assesses whether you can identify root causes of errors and explain fixes precisely enough to guide model training.
Expect something like: “AI methods section omits key statistical details from protocol. How would you structure feedback explaining what's missing, why it matters, and what AI should reference?”
Exercise you may get
Evaluate AI-generated manuscript section against publication guidelines and source data. Identify compliance gaps, trace claims to sources, distinguish errors from preferences, and provide structured feedback.
How to prepare
- Review ICMJE, CONSORT, STROBE, and GPP guideline documents specific to your therapeutic area
- Gather and review 2-3 recent peer-reviewed manuscripts you authored or reviewed to ground yourself in publication standards and data reporting practices
- Study examples of flawed AI-generated medical writing to identify common error patterns and think about how you would provide feedback
- Prepare a sample clinical study report or dataset you can reference to practice tracing claims to source data and catching misrepresentations
Facts
- Pay
- $50–80/hr
- Eligible locations
- Bangladesh, Hong Kong, India, Indonesia, Japan, Kazakhstan, Kyrgyzstan, Malaysia, Pakistan, Philippines, Singapore, Sri Lanka, Taiwan, Thailand, Uzbekistan, Vietnam, Austria, Belarus, Belgium, Denmark, France, Germany, Greece, Italy, Netherlands, Portugal, Russia, Spain, Switzerland, United Kingdom, Argentina, Brazil, Chile, Colombia, Mexico, Peru, Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Tunisia, United Arab Emirates, United States, Canada, Nigeria, Kenya, South Africa, Ghana, Ethiopia
- Domain
- Sciences Research
- Role type
- generalist
- Posted
- 9/18/2026
- Open slots
- 50