Oncology Clinical Researcher

Job summary

Review and evaluate AI-generated oncology content for clinical accuracy, scientific rigor, and regulatory alignment. Provide expert feedback to shape how AI models reason about cancer research, trial design, and data interpretation.

Job details

Oncology Clinical Researcher (AI Training) About The Role What if your deep knowledge of cancer clinical trials could directly shape how AI reasons about oncology — influencing research, treatment evaluation, and regulatory science for years to come? We're partnering with the world's leading AI research labs to build and train frontier AI models in oncology. We're looking for experienced Oncology Clinical Researchers to bring real-world expertise into AI-driven workflows — ensuring that the systems shaping the future of cancer research are grounded in rigorous, accurate, and clinically meaningful science. This is a fully remote, flexible contract role. No AI background needed — just deep oncology expertise and a sharp eye for clinical quality. Organization: Alignerr Type: Hourly Contract Location: Remote Commitment: 10–40 hours/week What You'll Do Review and evaluate AI-generated oncology content for clinical accuracy, scientific rigor, and regulatory alignment Apply your trial design expertise to assess AI outputs related to study protocols, patient enrollment, and endpoint selection Analyze and validate AI-generated interpretations of cancer trial data — including safety profiles, efficacy outcomes, and biomarker findings Evaluate whether AI-produced regulatory content meets the standards expected for FDA, EMA, or comparable agency submissions Provide structured, expert feedback that directly shapes how AI models understand and reason about oncology Work independently and asynchronously — fully on your own schedule Who You Are Experienced in designing and managing oncology clinical trials from protocol development through data readout Strong command of oncology clinical data analysis — endpoints, safety profiles, biomarkers, and statistical interpretation Familiar with regulatory submission standards for agencies such as the FDA or EMA Detail-oriented and rigorous — you notice when clinical reasoning doesn't hold up Able to communicate complex scientific judgments clearly and concisely in writing No prior AI or tech experience required Nice to Have Experience with data annotation, data quality review, or evaluation systems Background in translational oncology, biomarker research, or precision medicine Familiarity with AI tools or clinical decision support systems as an end user Experience contributing to scientific publications or regulatory dossiers Why Join Us Work directly on frontier AI systems transforming how the world approaches cancer research Fully remote and flexible — work when and where it suits you Freelance autonomy with meaningful, substantive work that draws on your deepest expertise Influence how AI models understand real oncology data at a foundational level Collaborate with a global network of leading researchers and AI scientists Potential for ongoing work and contract extension as new projects launch

What you’ll do

Review and evaluate AI-generated oncology content for clinical accuracy, scientific rigor, and regulatory alignment. Provide expert feedback to shape how AI models reason about cancer research, trial design, and data interpretation.

Requirements

Requires deep expertise in designing and managing oncology clinical trials and a strong command of clinical data analysis. Candidates must be familiar with FDA and EMA regulatory submission standards and possess strong written communication skills.

Listed skills

• Évaluation · Preferred
• precision · Preferred
• analysis · Preferred
• Organization · Preferred
• Detail-oriented · Preferred
• Data analysis · Preferred
• safety · Preferred
• Communication · Preferred
• Accuracy · Preferred
• Development · Preferred
• future · Preferred
• Flexible · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Oncology Clinical Trial Design
• Clinical Data Analysis
• Regulatory Submission Standards
• FDA Regulations
• EMA Regulations
• Protocol Development
• Biomarker Analysis
• Safety Profile Evaluation
• Efficacy Outcomes
• Data Annotation
• Scientific Writing
• Clinical Quality Review

Job areas

• Science & Research
• Healthcare
• Technology
• Data & Analytics