Insurance Verification & Benefit Manager

$105/hrrate as listed

Review and annotate AI-generated insurance eligibility and benefits outputs against real payer rules, EDI 270/271 responses, and coordination-of-benefits logic. Mercor is staffing this for an AI research lab; observed pay is around $105/hr on contract, project-dependent.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.

What the work involves

You will be handed AI-generated artifacts from front-end revenue cycle workflows — parsed 271 responses, benefit summaries, coverage determinations, COB resolutions, prior-auth triggers — and asked to judge whether they are correct, complete, and defensible against the actual payer's rules. Much of the day is close reading: does the model's benefit summary match what the 271 loop actually returned, or did it hallucinate a copay tier? Did it catch that the patient has a Medicare Advantage plan rather than traditional Part B, and route the verification accordingly? You will write structured annotations explaining not just that an output is wrong but why a verification specialist would catch it, since the written rationale is what the training pipeline consumes.

Expect a mix of task types across a project: scoring individual outputs, comparing two model responses head-to-head, and occasionally authoring reference scenarios and SOPs that become gold-standard data. Volume and task design shift as the lab's priorities change.

What the platform screens for

Verifiable operational history — specific payers, clearinghouses, and EHRs you have worked in, and what your team's front-end denial rate looked like.
Depth under follow-up — the screen will push past the acronyms. Knowing 270/271 exists is table stakes; explaining what a service type code 30 response actually omits is the differentiator.
Evaluation judgment — whether you can separate "technically inaccurate" from "materially harmful," and articulate a consistent rubric rather than gut reactions.
Written clarity — annotations that a non-clinical ML engineer can act on.

CHAM or CHAA helps but does not substitute for hands-on payer work. Management experience matters mainly because it usually means you have owned the KPI and the root-cause analysis behind it.

Logistics

Fully remote and largely asynchronous, with work delivered through Mercor's task platform. Contributors commonly commit 10–20 hours per week, though some projects offer more; hours are self-scheduled against task deadlines rather than fixed shifts. Engagements are contract-based and scoped in waves, so continuity depends on the lab's roadmap. Occasional synchronous calibration calls with the research team are typical early in a project. Do not use real PHI in any submission — all work should use synthetic or fully de-identified scenarios.

What they look for

5+ years in insurance verification, eligibility, or front-end revenue cycle operations, including 2+ years managing a team
Expert working knowledge of EDI 270/271 real-time eligibility transactions and payer portal navigation
Deep familiarity with Medicare, Medicare Advantage, Medicaid, and commercial benefit structures and COB rules
Hands-on experience with Epic, Cerner, or Meditech plus a clearinghouse such as Availity or Change Healthcare
Precise written English capable of explaining a coverage error to a non-clinical audience
CHAM, CHAA, or equivalent front-end revenue cycle certification (preferred)

Before you apply

Would you pass this screen?

Drawn from what this listing actually requires. Answered in your browser — nothing is sent to us, and we do not store your answers.

Do you have 5 or more years of direct experience in insurance verification, eligibility and benefits management, or front-end revenue cycle operations?
Insurance verification experience · required
Do you have at least 2 years of experience in a management role overseeing insurance verification, eligibility, or front-end revenue cycle teams or functions?
Management experience in eligibility · required
Do you have hands-on working knowledge of EDI 270/271 real-time eligibility transactions and experience submitting or troubleshooting them in a production environment?
EDI 270/271 expertise · required
Are you currently proficient in at least one of the following EHR platforms: Epic, Cerner, or Meditech?
EHR platform proficiency · required
Have you worked with at least one major clearinghouse platform such as Availity, Change Healthcare, Emdeon, or equivalent for eligibility verification?
Clearinghouse experience · preferred
Do you hold or have you earned a CHAM, CHAA, or equivalent front-end revenue cycle certification?
Revenue cycle certification · preferred

0 of 6 answered

Included with this listing

Interview prep — 11 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: Can you distinguish between primary and secondary payer responsibility, and do you have hands-on experience preventing CoB errors before they reach claims?
A strong answer: A strong answer names a specific payer scenario (e.g., Medicare primary with commercial secondary, or spouse's employer plan), describes the coverage overlap you caught, and explains the concrete action you took to correct it—not just that you flagged it, but how you ensured the claim posted correctly.
Where people lose it: Vague answers like 'I checked coordination of benefits' or describing only the discovery without the resolution step. The interviewer needs proof you actually *fixed* CoB problems, not just spotted them.