Quick facts
- Pay
- $30 - $60/hour
- Type
- Part-time
- Location
- Remote, worldwide
- Background
- PhD
- Platform
- Micro1 — 295 open jobs
- Typical pay at Micro1
- $20–300/hour · median $68 (290 jobs on our site)
- Payout
- Deel (twice a month)
- Apply
- Free to apply — never pay to get AI training work
Pay: $30 - $60/hour
Required Skills
Research & source triangulation
Attention to detail
Written precision
Self-direction & reliability
Analytical thinking
Research mastery
About micro1
micro1 is a data lab that helps AI labs & Enterprises train foundational models and agents. We provide frontier evaluations and reinforcement learning environments to improve LLM capabilities, as well as contextual evaluations to monitor and improve AI agents in enterprise settings.
Our AI recruiting agent sources and vets domain experts for our data platform that enables rapid production of high-quality training data. Our research spans high-value, specialized domains (legal, medical, finance, coding, etc.), where expert-level data is the hardest to produce and most valuable for advanced model performance. Our pipeline performance system ensures both quality and velocity.
Role Title: Research Evaluation Specialist (PhD or Masters / Researcher)
Role Type: Contractor (~15 hs a week)
Location: Remote, worldwide
micro1 is engaging Research Evaluation Specialists (PhD or Masters / Researcher) to contribute expert insight in developing rigorous test material for advanced AI model evaluation as part of a customer's project. 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. Prior experience in AI is required while your domain knowledge brings high value.
Scope of Work
Create original, high-difficulty question-and-answer pairs at the cutting edge of your discipline to rigorously assess AI model capabilities.
Source, verify, and thoroughly document answers using primary sources and authoritative references, including clear citations and reasoning.
Design questions demanding advanced reasoning, methodological nuance, or synthesis across multiple sources, ensuring resistance to oversimplified or shortcut solutions.
Test questions against AI systems, identify items that are not sufficiently challenging, and iteratively increase complexity while maintaining accuracy.
Write each question and answer with high precision and clarity, eliminating ambiguity and supporting defensible responses.
Integrate reviewer feedback and consistently uphold all project guidelines and quality standards throughout your deliverables.
Preferred Qualifications
Masters or PhD completed, active candidacy, or equivalent research experience as a specialist, researcher, assistant professor or professor in your domain.
Demonstrated track record of scholarly research, in-depth subject matter expertise, and familiarity with primary literature.
Exceptional analytical thinking, close attention to detail, and strong written communication skills in English.
Experience sourcing and triangulating information from authoritative references.
Proven self-direction and reliability in delivering expert-level work independently within remote engagements.
Skill in composing challenging, original, and methodologically sound questions.
Prior experience in AI training, data labeling, or model evaluation is required.
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. Minimum submission requirements apply. Experts must submit a minimum of tasks per week.
Start Timeline & Availability
We typically fill roles within 48 hours and are looking for experts ready to jump in right away. If selected, we expect you to start your first tasks within 24–48 hours of completing onboarding.
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micro1 recruits engineers and domain experts for AI labs using its AI interviewer. Once vetted, contractors work on human-data projects such as writing and checking model outputs or building coding evaluations.