About this role
In this hourly, remote contractor role, you will work as a Clinical Researcher Subject Matter Expert (SME) to review AI-generated clinical research and biomedical content and/or create expert research content, evaluating reasoning quality and step-by-step scientific problem-solving while providing precise written feedback. You will assess solutions for accuracy, clarity, safety, and adherence to the prompt; identify errors in study design, statistics, endpoint selection, bias/confounding, clinical trial methodology, and evidence interpretation; fact-check medical and scientific claims; write high-quality explanations and model solutions that demonstrate correct research reasoning; and rate and compare multiple responses based on correctness and reasoning quality. This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your clinical research expertise directly helps improve the world’s premier AI models by making their evidence appraisal, trial interpretation, and research communication more accurate, reliable, and clearly explained. Important: There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network. Requirements - Bachelor’s degree (or higher) in a relevant field (e.g., Public Health/MPH, Epidemiology, Biostatistics, Clinical Research, Biomedical Sciences, PharmD, MD/DO, RN, or related). - 5+ years of professional experience in clinical research (academia, hospitals, CROs, biopharma, public health research, or similar). - Strong command of clinical research fundamentals: study design, endpoints, inclusion/exclusion criteria, power/sample size intuition, and interpretation of results. - Comfortable evaluating statistical reasoning (effect sizes, confidence intervals, p-values, multiple comparisons, missing data, sensitivity analyses). - Deep understanding of bias, confounding, and causality (randomization, blinding, selection bias, measurement bias, survivorship, collider bias). - Familiarity with clinical trial operations concepts (protocols, deviations, AE/SAE logic, data quality, monitoring) and/or GCP principles is a plus. - Exceptional attention to detail when fact-checking; minimum C1 English proficiency with strong scientific writing. - Reliable, self-directed, and able to deliver consistent quality in an hourly, remote contractor workflow across time zones. - Prior experience with AI data training/annotation, medical/scientific editorial QA, systematic reviews, or guideline work is strongly preferred. Responsibilities - Develop AI Training Content: Create detailed prompts and gold-standard answers across clinical research methodology, interpretation, and scientific communication. - Optimize AI Performance: Evaluate and rank AI responses to improve correctness, rigor, clarity, and contextual relevance. - Ensure Model Integrity: Test AI outputs for inaccuracies, misleading evidence interpretation, unsafe recommendations, and bias; validate reliability across use cases. Skills - Bias & Confounding - Clinical Trial Design - Biostatistics - Regulatory/GCP Awareness - Study Methodology - Evidence-Based Medicine - Data Interpretation - Confounding - Epidemiology - Literature Review - English - Bias Additional skills - Clinical Trial Design - Epidemiology - Biostatistics - Study Protocol Development - Endpoint Definition - Inclusion/Exclusion Criteria - Sample Size Estimation - Power Analysis - Statistical Interpretation - Effect Size Analysis - Confidence Intervals - Hypothesis Testing (p-values) - Multiple Comparisons - Missing Data Methods - Sensitivity Analyses - Bias & Confounding Assessment - Causal Inference - Randomization - Blinding - Selection Bias - Measurement Bias - Good Clinical Practice (GCP) - Clinical Trial Monitoring - Adverse Event (AE) Reporting - Serious Adverse Event (SAE) Reporting - Protocol Deviations - Data Quality Management - Scientific Writing - Systematic Reviews - Medical Editing / Editorial QA
