Medical Coder Experts: $45-$110/hr from AI Labs

Open application· 76 spots this round

$45-$110/hr medical coding work, on your schedule

Review AI-generated codes, modifiers, and claims the way you'd review a coder's queue before submission. Flag the upcode, the unbundling, the documentation that won't support the claim. The accuracy that keeps claims clean and compliant is exactly what AI labs need.

Fully remoteYour scheduleWeekly pay

Apply nowApply once, get matched on a rolling basis. No prior AI experience needed.

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Hi, we're Zac and Jack, the founders of Terac. We want to talk to you directly, because you are the most important part of what we're building.

Terac is a community of experts. People who have spent years getting good at something specific and hard. The world is about to need more of you, not less. As AI takes on more of the world's work, the bottleneck shifts to the people who actually know what they're talking about.

Expert labor is the rarest resource in the world right now, and it is shockingly hard to find. The companies that need a CPC coder's eye on an unbundled claim spend weeks chasing people, paying placement fees, and settling for whoever is available. Meanwhile thousands of qualified people are sitting with knowledge that no one ever asks for.

That gap is what we're here to close. Every project that lands on Terac is routed to the people who actually know the answer, on their schedule, paid fairly, and only when the work is verified. No middleman taking a cut of your time. No vague gigs. No chasing checks.

We care about every single person in this community. If you join Terac, you're not a row in a database to us. We read the feedback. We answer the emails. We will fight for you when a customer is being unreasonable, and we will be honest with you when something on our side is broken. The quality of this panel is our entire company, and we owe you a serious bar.

If you've made it this far, here is what we're asking: claim your profile. Put your expertise on the record. Let the world's most ambitious teams come find you for the work only you can do.

Zac & Jack

Founders

Medical Coding questions

Still curious? Write to us at support@terac.com.

Both credentials qualify, and outpatient professional-fee coding is actively useful because a large share of AI-generated coding work involves physician practice and clinic settings where CPT and ICD-10-CM are the primary code sets. CCS holders who work with UB-04 claims and DRG assignment are equally in demand for inpatient chart review tasks. You do not need both credentials to participate.

Tasks are encoder-agnostic and focus on your knowledge of the official code sets, ICD-10-CM/PCS guidelines, CPT Editorial Panel conventions, and AHA Coding Clinic guidance rather than any proprietary software interface. You will read clinical documentation and AI-assigned codes in plain text and evaluate accuracy, sequencing, and guideline compliance without needing to log into an encoder platform.

The work does not involve accessing real patient records, submitting claims, or advising on live billing decisions, so it falls outside the scope of activities regulated by the AHIMA Standards of Ethical Coding or the AAPC Code of Ethics. You are evaluating synthetic or de-identified training scenarios and explaining expert reasoning, which is analogous to writing a coding textbook or teaching a certification prep course.

Specialization is an advantage, not a liability. Risk adjustment coders familiar with CMS-HCC model hierarchies and chronic condition documentation requirements, oncology coders who work with C-codes and surgical approach distinctions, and CDI-adjacent coders who understand query workflows are particularly useful for evaluating AI outputs in those domains where errors are most consequential. You will be matched to task types that align with your documented specialty experience.

You will typically work with realistic clinical documentation excerpts such as operative reports, H&P notes, discharge summaries, and radiology or pathology reports, alongside the AI-assigned code sets and any rationale the model generated. Your job is to assess whether the principal diagnosis selection, secondary diagnoses, procedure codes, and sequencing conform to current Official Guidelines for Coding and Reporting and, where relevant, payer-specific rules. Some tasks also ask you to write out your own coding rationale step by step so the model can learn how an experienced coder moves through a chart.

Why your expertise matters

Medical coding is one of the highest-stakes domains for AI accuracy: a single code swap between a principal diagnosis and a comorbidity can flip a DRG, trigger a RAC audit, or deny a claim entirely. AI models trained without credentialed coder review routinely hallucinate specificity - assigning codes to a level of detail not supported by documentation, or missing CC/MCC capture that changes reimbursement by thousands of dollars. Your judgment on whether an AI-assigned ICD-10-CM, CPT, or HCPCS code is defensible against payer scrutiny is exactly what frontier models need to become clinically reliable.

How pay works

Work toward the top of the $45-$110/hr band by bringing active credentials (CPC, CCS, CIC, or COC), multi-specialty or inpatient facility coding experience, and deep familiarity with audit-level documentation requirements such as MS-DRG optimization, CC/MCC validation, or hierarchical condition category (HCC) risk adjustment. All work is fully remote and paid hourly on verified task completion - no minimum hours, no retainer, and no background in AI is expected before you start.

What the work looks like

A sample of the medical coding work you would pick up. Every project is scoped, remote, and paid on verified completion.

Review an AI-generated ICD-10-CM and CPT code set for a complex E&M note and flag any codes assigned without adequate documentation support under the 2024 AMA guidelines.
Annotate a set of operative reports where an AI model selected incorrect principal procedure codes under ICD-10-PCS, explaining which root operation and body part values should apply and why.
Evaluate AI-produced HCC coding recommendations against the underlying progress notes and mark any suspect HCC captures that would not survive a risk adjustment data validation audit.
Create a worked example for a multi-trauma inpatient case showing the sequencing logic for principal diagnosis selection under UHDDS guidelines, written to teach a model how an experienced inpatient coder reasons through conflicting diagnoses.
Score a batch of AI-assigned modifier combinations on outpatient surgical claims, noting each instance where a 59 modifier was applied without meeting payer criteria for distinct procedural service.
Stress-test an AI coding assistant by submitting ambiguous cardiology catheterization reports and documenting cases where the model over-specified a code beyond what the physician documentation actually supports.