Micro1
Micro1Verified listing
RemoteHot

Medical Research Technical Staff

7–8/hr
Remote
Posted September 23, 2026
full-time
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Listing checked October 12, 2026 · pay as published by Micro1

Overview

micro1 seeks a Domain Researcher to build evaluation frameworks for AI systems used in healthcare and biomedical settings. You will design benchmarks, scoring rubrics, and validation protocols that test medical reasoning, evidence synthesis, and clinical decision support. The role sits at the intersection of clinical research and enterprise AI, with a focus on agentic medical workflows. This is a full-time, remote position.

What You'll Do8

  • 1Create and own evaluation frameworks for AI systems in healthcare, covering benchmarks, scoring methods, clinical quality rubrics, and validation protocols.
  • 2Research medical reasoning, evidence-based decision making, clinical workflows, and healthcare information synthesis.
  • 3Build benchmark datasets and evaluation environments that reflect real-world healthcare and biomedical scenarios.
  • 4Work with researchers, clinicians, and engineers to assess and improve healthcare-focused AI systems.
  • 5Examine performance, safety considerations, reasoning quality, and failure patterns in medical use cases.
  • 6Write research reports, evaluation methodologies, and best-practice guidelines.
  • 7Track developments in medicine, healthcare delivery, biomedical research, and AI.
  • 8Help shape industry standards for healthcare AI evaluation and reliability.

Requirements6

  • 1Advanced degree in Medicine, Public Health, Nursing, Pharmacy, Biomedical Sciences, Epidemiology, Health Economics, or a related healthcare field (MD, DO, PhD, MPH, PharmD, etc.).
  • 2Deep expertise in clinical practice, healthcare operations, biomedical research, public health, healthcare analytics, or evidence-based medicine.
  • 3Proven experience conducting medical, clinical, or healthcare research.
  • 4Strong grasp of research design, evidence evaluation, and healthcare decision-making processes.
  • 5Exceptional analytical reasoning and scientific communication skills.
  • 6Experience in multidisciplinary environments involving research, healthcare, technology, or policy stakeholders.

Who Should Apply

This role fits someone with an advanced clinical or biomedical degree who has run medical research and wants to apply that rigor to AI evaluation. You should be comfortable designing studies, judging evidence quality, and explaining clinical reasoning to engineers and researchers. The work is less suitable for candidates without hands-on healthcare research experience or those who prefer purely technical AI roles without clinical depth. A common reason candidates get rejected is lacking a clear track record of medical or clinical research, or having no familiarity with healthcare quality measurement, clinical guidelines, or evidence synthesis. Another is not showing how their clinical expertise translates into building evaluation frameworks for AI systems.

Salary Insight

The source lists a rate of $7 to $8 per hour. No other compensation details are provided in the posting.

Pay and demand for Science & Research roles

Aggregated

Typical pay

$70/hour

This role

$7–$8/hr

Most Science & Research roles pay $40–$100 per hour. This role's pay sits below that range.

Based on 988 similar roles that publish pay · 301 publish only a top rate; those count at the rate they gave

Typical rangeMedian payThis role

Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.

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Most requested skills · share of roles

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  • ai evaluation
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Location

TypeRemote
LocationRemote
This is a remote position

Compensation

$7–8/hr

Required Skills

Medical/Health ResearchEnterprise AIBenchmarkingAgentic Medical Workflows

Application Tip

Highlight specific medical research projects where you designed evaluation criteria, rubrics, or benchmarks, and name the clinical domains and evidence synthesis methods you used. Quantify outcomes where possible, such as the number of studies reviewed or the size of datasets you built.

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