Oncology Clinical Researcher

Job summary

Design and evaluate oncology clinical trial frameworks, analyze trial safety, efficacy, and biomarker data, and translate findings into regulatory and scientific narratives. Review AI-generated oncology insights for clinical accuracy, scientific validity, and regulatory alignment, and help improve AI systems' reasoning about oncology data.

Job details

Oncology Clinical Researcher (AI Training) About The Role What if your deep expertise in cancer clinical trials could directly shape how AI reasons about oncology — influencing the tools that will define the next era of cancer research and care? We're looking for experienced Oncology Clinical Researchers to bring real-world clinical rigor into AI-driven research workflows. You'll evaluate, validate, and improve how advanced AI systems understand oncology trial data — ensuring that the science behind cutting-edge AI reflects genuine regulatory, clinical, and scientific standards. This is a fully remote, flexible contract role built for experienced oncology professionals who want to do meaningful work on their own schedule. Organization: Alignerr Type: Hourly Contract Location: Remote Commitment: 10–40 hours/week What You'll Do Design and evaluate oncology clinical trial frameworks — including study protocols, patient enrollment criteria, and regulatory compliance considerations Analyze cancer trial data covering safety endpoints, efficacy outcomes, and biomarker results to assess treatment performance Translate complex trial outcomes into clear regulatory and scientific narratives aligned with FDA/EMA submission standards Review and critique AI-generated oncology insights for clinical accuracy, scientific validity, and regulatory alignment Apply your domain expertise to improve how AI models understand and reason about real-world oncology data 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 background in analyzing clinical oncology data — including primary and secondary endpoints, safety profiles, and biomarkers Familiar with regulatory submission standards for agencies such as the FDA or EMA Detail-oriented and rigorous — you hold AI-generated content to the same standards you'd apply in a real clinical setting Comfortable working independently on structured, task-based assignments Nice to Have Prior experience with data annotation, data quality evaluation, or AI model assessment workflows Background in oncology publications, clinical decision support, or medical writing Experience across multiple cancer types or therapeutic modalities Why Join Us Work directly on frontier AI systems that are transforming how cancer research is understood and conducted Influence how AI models reason about real oncology data — at a scale no individual trial could achieve Fully remote and flexible — work when and where it suits you Freelance autonomy with the structure of meaningful, impactful work Collaborate with world-leading AI research teams and labs Potential for ongoing work and contract extension as new projects launch

What you’ll do

Design and evaluate oncology clinical trial frameworks, analyze trial safety, efficacy, and biomarker data, and translate findings into regulatory and scientific narratives. Review AI-generated oncology insights for clinical accuracy, scientific validity, and regulatory alignment, and help improve AI systems' reasoning about oncology data.

Requirements

Candidates should have experience designing and managing oncology clinical trials from protocol development through data readout, along with strong skills in analyzing clinical oncology data and biomarkers. They should be familiar with FDA or EMA submission standards, detail-oriented, and comfortable working independently on structured assignments.

Listed skills

• Regulatory Compliance · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Oncology Clinical Trial Design
• Clinical Trial Protocol Development
• Patient Enrollment Criteria
• Regulatory Compliance
• Clinical Data Analysis
• Safety Endpoint Analysis
• Efficacy Outcome Analysis
• Biomarker Analysis
• FDA/EMA Submission Standards
• Scientific Writing
• AI Output Evaluation
• Clinical Accuracy Assessment
• Scientific Validity Assessment
• Data Annotation
• Data Quality Evaluation
• Medical Writing

Job areas

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

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