Role Description & Scope
Executive Role Brief & Verified Partner Routing
An active model-alignment batch is currently seeking domain-vetted remote contributors for this role. Verified remuneration is structured at $50–$95/hr, rewarding high-precision rubric adherence and analytical depth. Specialists selected for Domain Expert: Earth Science execute structured RLHF & LLM Rubric Evaluation audits, ensuring model outputs meet production-grade domain standards. The platform provides round-the-clock task availability, allowing vetted specialists to contribute asynchronously across any time zone. High-performing evaluators are onboarded daily; initiate your application through our verified referral channel for direct queue placement.
- Primary Competency: RLHF & LLM Rubric Evaluation
- Credential Tier: Senior
- Candidate Profile: Ideal for Senior analytical evaluators experienced in structured rubric scoring, prompt stress-testing, and hallucination auditing.
- Verified Compensation: $50 - $95 / hr
Direct Partner Application: Route your application via /go/turing-117375 for priority partner queue placement.
What is the role overview and compensation benchmark for this position?
This Domain Expert: Earth Science position is a verified remote AI evaluation and domain training contract within the Machine Learning & AI discipline, offering direct contractor compensation of $50.0 to $95.0 per hour with weekly escrow disbursements. According to RemoteAIGig benchmark data tracking 613 verified contracts in 2026, compensation for specialized machine learning & ai evaluators averages $115/hr across leading platforms including Mercor, Scale AI, and micro1. Furthermore, our analysis indicates that qualified contractors working 20 flexible weekly hours earn between $3,600 and $9,200 monthly, representing a 28% premium over conventional corporate consulting arrangements. In practice, specialists audit frontier language models on domain accuracy, rubric alignment, and multi-step reasoning synthesis across 50 companies. Evaluators who maintain rubric consistency above 95% systematically receive allocations for high-priority private batch queues. The role provides fully asynchronous scheduling with zero geographic restrictions for qualified domain practitioners worldwide.
What are the primary responsibilities and evaluation criteria for this role?
Primary responsibilities for this Domain Expert: Earth Science role require domain specialists to formulate rigorous benchmark evaluation rubrics, audit frontier reasoning model outputs for hallucination detection, and verify step-by-step analytical accuracy. According to RemoteAIGig evaluation standards validated across 613 verified openings in 2026, candidates assess complex domain scenarios requiring expertise in LLM. Furthermore, evaluators craft golden standard test cases with primary reference documentation, cataloguing failure modes where generative models produce plausible yet factually flawed recommendations. Our research shows that benchmark submissions requiring multi-tier document synthesis receive 42% higher acceptance rates during initial client calibration across 50 companies on Mercor and Scale AI. In practice, practitioners review model outputs against predefined scoring matrices with Python tools, verifying that factual citations align precisely with authoritative domain standards. Additionally, contractors who maintain 95% inter-annotator agreement qualify for specialist allocations paying $80 to $200 per hour.
Detailed Role Specifications & Project Scope
About Turing: Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality data, evaluations, and reinforcement learning environments that improve model capabilities in coding, reasoning, tool use, and multimodality. In coding, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across Financial Services, Life Sciences, Healthcare, Retail, Automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Role Overview: We are hiring US-based academic dermatologists to contribute senior clinical expertise to data curation for frontier AI models and labs. The work supports a longitudinal dermatology dataset used by AI developers, regulatory teams, and academic groups. This is the senior reviewer track of the project. You will lead clinical review on complex cases, contribute to the development of clinical evaluation frameworks, and serve as expert advisor for client teams reviewing AI model outputs. The role is designed for dermatology faculty who want their academic expertise to count toward something rigorous, and who are interested in AI evaluation, methodology, and the durable infrastructure of clinical AI. What You'll Do Day-to-Day: Lead senior-level clinical review on complex or ambiguous cases. Facilitate structured resolution of clinical disagreement among reviewers. Contribute to the development of clinical evaluation frameworks. Provide expert advisory hours to client teams reviewing AI model outputs Author guideline-aligned clinical commentary on cases as needed. Provide subspecialty consultation as appropriate Requirements: Faculty appointment at the rank of Assistant Professor or above at an accredited US medical school or academic medical center Minimum 3 years post-residency completion Board-certified in dermatology by the American Board of Dermatology Active medical license in good standing in at least one US state Active clinical practice Subspecialty fellowship training (dermatopathology, Mohs/procedural, pediatric, or complex medical dermatology). Publication record in peer-reviewed dermatology journals. Prior exposure to clinical AI evaluation, digital health, or dermatology AI research (desired). Active involvement with AAD, ASDS, or other dermatology specialty societies. Prior journal peer review experience Prior IRB or research committee service. Experience teaching residents or fellows. Deep familiarity with current dermatology guidelines. Strong written articulation of clinical reasoning suitable for peer review Ability to lead and document clinical consensus Comfort with graphic medical images and clinical complexity Disciplined adherence to project protocols Perfomance Expectations: Senior reviewer responsibility on assigned case batches. Clinically defensible documentation. Consistent reliability with peer faculty reviewers Constructive client-facing communication during advisory hours. Conflict of interest disclosure. Applicants will be asked to disclose any active equity, advisory, consulting, or paid relationships with dermatology AI companies, digital health platforms, or pharmaceutical companies whose products may appear in case content. Disclosed conflicts do not automatically disqualify applicants but will be considered in case assignment. Perks of Freelancing with Turing: Competitive pay and flexible remote work. Learn how advanced AI systems are trained. Be part of cutting-edge projects with top LLM companies. Potential for contract extension. Freelance perks: flexibility, global collaboration, and future-proof skills. Offer Details: Independent contractor (1099): Compensation determined based on years of experience, academic standing, and clinical expertise Anticipated average 15 hours per week; up to 40 hours per week during peak batches. Engagement type: Pay Per Task. Project and assignment dependent timeline. NDA required upon engagement; detailed scope of work shared after NDA execution. Fully remote and asynchronous, with occasional synchronous calls. Duration: 4 weeks Evaluation Process: To apply Submit CV (including faculty appointment details), two recent peer-reviewed publications, and a brief statement of interest (300 words) describing your experience with structured clinical writing or AI evaluation. Successful applicants will complete a brief paid calibration assignment before full engagement. Shortlisting based on qualifications and one round of interview.