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Salary: USD 175,000 - 250,000 / annual
Translucent is an AI-native financial platform built exclusively for healthcare providers. The company was founded in 2024 and is backed by GV, NEA, FPV, and Virtue. Translucent has already been deployed by healthcare organizations managing over $5 billion in combined revenue.
As Subject Matter Expert for Revenue Cycle, you will bring deep domain expertise into the product itself, shaping how AI agents reason about denials, underpayments, and charge capture. You'll define what correct answers look like and hold model output to the standard a working revenue cycle leader would apply.
Key responsibilities include:
- Develop and deliver subject matter expertise in healthcare revenue cycle to support AI product development and customer margin realization
- Work closely with engineering, product, and design teams to define and develop AI systems for revenue cycle workflows
- Build proprietary benchmarks and datasets to evaluate models and AI agents against real-world revenue cycle tasks: denial root-cause classification, underpayment detection, expected-versus-paid reconciliation, charge capture review, and prior-authorization triage
- Partner with customer delivery to understand revenue cycle operations, identify pain points, and translate complex financial and operational requirements into technical solutions
Required qualifications:
- 7+ years in provider revenue cycle, patient financial services, or equivalent function, ideally with exposure to denials, underpayment recovery, charge capture, and payor compliance
- Ability to convert workflows and hypotheses into structured data, rules, and logic for algorithms and quality measurement
- Strong proficiency with revenue cycle systems (Epic Resolute, Cerner/Oracle Health, or similar) and fluency in 835/837 transactions, claim adjustment reason codes, and appeal construction
- Demonstrated ability to deliver high-quality analyses on demanding deadlines
- Ability to effectively communicate with internal and external stakeholders and translate complex problems between revenue cycle, product, and engineering teams
- Ability to define positive outcomes in situations with underspecified success criteria
- Deep intellectual curiosity and eagerness to learn across domains, particularly at the intersection of revenue cycle and AI
- Willingness to do hands-on work: grading model-generated denial classifications, hand-adjudicating flagged accounts to establish ground truth, stress-testing outputs
Nice to have: experience at a high-growth startup, familiarity with SQL/Python or other data tools, prior exposure to AI/ML concepts or prompt engineering.
