Pharmacy Technician - AI Trainer

Obsidian; Houston (TX); On-site; USD 28,000 - 55,000; Full time

Job summary

Mercor is collaborating with a leading AI research lab to hire certified pharmacy technicians (CPhT) for reviewing medication-related content and evaluating AI-generated responses for accuracy and safety. You will annotate and provide feedback to ensure high-quality clinical guidance and consistent application of guidelines.

The role focuses on medication history, prior authorisation workflows, dosing, interactions, and pharmacy workflows.

Qualifications

• Certified Pharmacy Technician (CPhT) or equivalent experience.
• Hands-on experience with medications, prescriptions, and pharmacy operations.
• Strong knowledge of medication safety, drug interactions, and best practices.
• Excellent written communication skills and attention to detail.
• Comfortable following structured annotation guidelines.
• Available for 30–40 hours per week during the project.

Responsibilities

• Answer medication-related clinical questions using patient context, source materials, and labeling guidelines.
• Review AI-generated responses for clinical accuracy, safety, clarity, completeness, and evidence support.
• Correct inaccurate or incomplete responses and provide structured annotations and feedback.
• Identify escalation cases with insufficient evidence, conflicting guidance, or safety concerns.
• Apply project rubrics and quality standards during calibration and feedback sessions.

Skills

CPhT

Pharmacy operations

Medication safety

Written communication

Annotation guidelines

Availability 30-40h

Job description

Mercor is hiring on behalf of a leading AI research lab developing next-generation clinical AI systems. We're seeking experienced Certified Pharmacy Technicians (CPhT) to support the development of AI-powered prior authorisation workflows by reviewing medication-related clinical content and evaluating AI-generated responses for accuracy, safety, clarity, and completeness. This role is ideal for pharmacy technicians with strong medication management and pharmacy operations experience who are interested in contributing to cutting-edge healthcare AI.

What You’ll Do

• Answer medication-related clinical questions using patient context, source materials, and labelling guidelines.

• Review AI-generated responses for clinical accuracy, safety, clarity, completeness, and evidence support.

• Correct inaccurate or incomplete responses and provide structured annotations and written feedback.

• Identify and escalation cases involving insufficient evidence, conflicting guidance, unclear clinical context, or patient safety concerns.

• Apply project rubrics and quality standards consistently while participating in calibration and feedback sessions.

Representative topics include:

• Medication history

• Prior authorisation requirements

• Medication indications

• Dosing information

• Contraindications

• Drug interactions

• Supporting clinical documentation

• Pharmacy workflows

Qualifications

• Certified Pharmacy Technician (CPhT) or equivalent pharmacy technician experience.

• Hands-on experience with medications, prescriptions, pharmacy operations, and prior authorisation workflows.

• Strong understanding of medication safety, drug interactions, and pharmacy best practices.

• Excellent written communication skills and strong attention to detail.

• Comfortable following structured annotation guidelines.

• Available for 30–40 hours per week during the project.

Preferred Experience

• Prior authorisation or speciality pharmacy

• Medication access programs

• Epic, Cerner, or other electronic health record (EHR) systems

• Clinical quality assurance

• AI evaluation, clinical content review, or healthcare data annotation

• 340B or medication assistance programs

What Success Looks Like

• Consistently accurate, evidence-based clinical annotations and reviews.

• High agreement with project quality standards.

• Reliable throughput and timely delivery.

• Clear identification and escalation of safety-critical or ambiguous cases.

• Rapid incorporation of calibration feedback.