Oncologist

AuraOne; United States; Remote; USD 482,000 - 620,000; Part time

Job summary

AuraOne is seeking an oncologist for a remote clinical-review track to evaluate AI outputs in oncology. You will assess differential diagnosis, dosing logic, and guideline adherence. You will document corrected clinical reasoning so the modeling team can train on it, and you will flag patient-safety issues using a structured rubric. This contractor role requires active oncology licensure and reliable availability for at least 10 hours per week.

Qualifications

• Active or recent oncology license/board certification.
• Experience reading patient cases and writing structured assessments.
• Ability to apply multi-page rubrics consistently.
• Clear written reasoning citing guidelines or evidence.
• Reliable availability for asynchronous work at least 10 hours per week.

Responsibilities

• Review AI outputs against current oncology guidelines.
• Grade differential reasoning, dosing, and red-flag handling.
• Flag patient-safety issues with severity tags.
• Capture corrected clinical reasoning for model training.
• Adjudicate disputes against published guidelines.
• Maintain reviewer scores in calibration cycles.

Skills

Clinical reasoning

Guideline review

Patient-safety assessment

Structured clinical writing

Oncology

Education

Active oncology license/board certification

Job description

Category: Medical & Clinical AI · Pay: $350–$450 / hr · Location: Remote — US-eligible · Contractor

Oncologist is a remote clinical-review track for evaluating AI outputs that touch oncology. Reviewers grade differential reasoning, dosing logic, and guideline adherence; flag patient-safety issues; and document the corrected clinical reasoning so the modeling team can close the gap.

Why this role matters

Oncology AI is only useful if a credentialed clinician would sign their name to the output. AuraOne uses licensed and credentialed reviewers to set the bar for clinical reasoning, dosing, and guideline adherence in regulated workflows.

Responsibilities

• Review AI outputs against current oncology clinical guidelines and standard of care for Oncologist assignments.
• Grade differential reasoning, dosing, and red-flag handling on a structured rubric.
• Flag patient-safety issues with severity tags that match clinical-incident taxonomies.
• Capture the corrected clinical reasoning so the modeling team can train on it.
• Adjudicate disputed cases against published evidence or specialty guidelines.
• Maintain reviewer-quality scores in weekly inter-rater calibration cycles.

Qualifications

• Active or recent license / board certification in oncology or an adjacent specialty for Oncologist work.
• Hands-on clinical experience reading patient cases and writing structured assessments.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that cites guidelines, evidence, or standard of care.
• Reliable async availability for at least 10 hours per week.

Example tasks

• Grade a model's differential-diagnosis reasoning on an oncology case and write a 2-3 sentence assessment.
• Flag a dosing miscalculation with the right severity tag and corrected dose.
• Adjudicate a disputed case between two reviewers using current specialty guidelines.
• Audit a 25-case batch for rubric consistency and report drift to the program lead.

Nice to have

• Prior work as a guideline reviewer, peer reviewer, or clinical educator.
• Familiarity with AI-assisted clinical decision support and its failure modes.
• Bilingual clinical experience for non-English patient cases.

Skills

• Clinical reasoning
• Guideline review
• Patient-safety assessment
• Structured clinical writing
• Oncology

Work model

Remote — US-eligible. Remote · Independent specialist contractor. Employment type: CONTRACTOR. Applicants must be authorized to work from US.

Compensation

$350–$450 / hr

Application process

Apply through AuraOne's specialist intake for role-specific routing and review. Final project scope, schedule, and contractor terms are confirmed before placement.