Subject Matter Expert, Revenue Cycle

ROLE DETAILS

Full-Time

Office Location — New York City (preferred)

WHY TRANSLUCENT

Healthcare providers drive $2.5 trillion in medical expenditures annually — and operate on razor-thin 2–5% margins. Despite these stakes, the finance teams behind these organizations are buried in spreadsheets, manual data pulls, and disconnected systems, spending more time finding and cleaning data than actually using it to make decisions.

Translucent is changing that. We’re building the AI-native financial platform designed exclusively for healthcare — giving every finance team, department, and service line their own arsenal of AI Agents that run 24/7, understand their specific data, business logic, and workflows.

Founded in 2024 and backed by GV, NEA, FPV, and Virtue, we’ve already been deployed by healthcare organizations managing over $5 billion in combined revenue. The product-market fit is real, the problem is massive, and we’re just getting started. If you want to work at the intersection of AI and one of the most complex, consequential industries in the world — this is the place.

ROLE OVERVIEW

Revenue cycle is where healthcare’s most auditable margin leakage lives, and where the distance between a flagged variance and a recovered dollar is operator judgment. As our subject matter expert for this domain, you will bring that judgment into the product itself, shaping how our agents reason about denials, underpayments, and charge capture, defining what a correct answer looks like, while holding model output to the standard a working revenue cycle leader would apply.

This position reports to Adam Albrecht, Head of Domain Intelligence.

WHAT YOU'LL DO

To facilitate the development of these systems, you will:

Develop and deliver subject matter expertise in healthcare revenue cycle to support AI product development and customer margin realization

Work closely with our 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 including, but not limited to: denial root-cause classification, underpayment detection, expected-versus-paid reconciliation, charge capture review and prior-authorization triage

Partner with customer delivery to understand our customer’s revenue cycle operations, identify pain points, and translate complex financial and operational requirements into technical solutions

WHAT YOU HAVE

7+ years in provider revenue cycle, patient financial services, or an 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 as well as the expertise to link data points that may not already speak to each other in ways that enhance performance

Strong proficiency with revenue cycle systems and the data underneath them (Epic Resolute, Cerner/Oracle Health, or similar), plus 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 a variety of 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 and desire to do work in the trenches — e.g., grading hundreds of model-generated denial classifications, hand-adjudicating flagged accounts to establish ground truth, stress-testing outputs against how a real appeal would actually land. Getting AI to do analyst-level revenue cycle work requires a lot of things that look like analyst-level revenue cycle work

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

Anticipated compensation: $175,000 – $250,000 and Equity