Medical Coding AI Evaluation Lead

About OpenTrain

OpenTrain is the #1 platform for people building careers in AI training and data labeling. We connect experienced contributors with specialized projects, help you track opportunities, and let you build a unified AI training portfolio you control.

For this role, OpenTrain is the hiring and contracting organization; you'll work as a contractor on projects that train and evaluate AI systems used in healthcare coding workflows.

Why AI training and medical coding matter

AI models depend on high-quality human-reviewed examples. In healthcare, accurate medical coding is critical for compliance, billing, and patient records—so your expertise directly shapes safer, more reliable AI-assisted coding tools.

This is flexible, remote work that lets experienced coders apply domain knowledge to improve model performance, reduce denial rates, and protect revenue integrity across professional fee and inpatient facility settings.

Role overview

You will serve as the Medical Coding AI Evaluation Lead, reviewing AI-generated coding assignments and auditing outputs to ensure accuracy, compliance, and appropriate DRG assignment. Your work will produce structured feedback and annotations used to train autonomous coding systems.

This is a part-time contractor role (20 hours/week) for U.S.-based contributors, paid at $80 per hour.

What you will do

Lead the evaluation and auditing of AI-generated coding across professional fee and inpatient facility workflows, identify errors, and provide targeted corrective feedback.

Monitor operational metrics related to coding quality and revenue integrity and collaborate with clinical documentation improvement (CDI), billing, and compliance stakeholders.

• Review AI-generated ICD-10-CM/PCS, CPT/HCPCS, and DRG assignments for accuracy and Official Coding Guidelines compliance.
• Conduct coding audits, document error patterns, and produce actionable feedback for coders and model training.
• Track productivity, accuracy rates, claim denials, unbilled accounts, and turnaround time to identify risks and improvement opportunities.
• Support coding education and quality-improvement initiatives tied to revenue integrity and compliance.
• Create structured annotations and evaluation ratings that help train and refine autonomous coding systems.

Requirements

Candidates must meet the stated experience and credential requirements and be able to work effectively in English as the primary language for documentation and feedback.

• 5+ years of medical coding experience, including at least 2 years in coding management or HIM leadership.
• Expert knowledge of ICD-10-CM/PCS, CPT/HCPCS, Official Coding Guidelines, and DRG assignment.
• Hands-on experience with professional fee and/or inpatient facility coding operations.
• Proven ability to audit coding work, identify compliance risks, and correct AI-generated coding errors.
• Familiarity with CAC tools, NLP-based coding platforms, and EHR workflows.
• Strong written and verbal English communication skills.
• CPC, CCS, RHIA, or RHIT credential preferred.

Helpful background

The following are not required but will help you excel and hit the ground running with quality measurement and stakeholder reporting.

• Experience presenting coding performance metrics to leadership and participating in revenue integrity discussions.
• Background designing or running coding quality-improvement programs and audits.
• Prior exposure to evaluating AI-generated coding assignments in a clinical or healthcare revenue-cycle environment.

Pay, schedule, and how it works

Pay rate: $80/hour. Time commitment: approximately 20 hours per week. Location: remote within the United States. Employment type: contractor, part-time.

OpenTrain contributors perform work that produces labeled and audited data used to train AI. You will submit evaluation ratings, written feedback, and structured annotations according to project guidelines and quality standards.

• Work remotely and set your schedule within agreed weekly hours for the project.
• Deliverables include audit reports, evaluation ratings, and structured textual annotations.
• All substantive details about tasks, submission format, and QA will be provided when you join the project.