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
Physician economics are shaped as much by compensation design and documentation behavior as by actual productivity, which makes benchmark comparisons easy to misread. As our subject matter expert for this domain, you will bring that judgment into the product itself, shaping how our agents reason about productivity, compensation, and access, while holding model output to the standard a working medical group 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 medical group and employed-physician operations 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 physician enterprise workflows
- Build proprietary benchmarks and datasets to evaluate models and AI agents against real-world physician enterprise tasks including, but not limited to: wRVU productivity analysis, benchmark comparison and normalization, compensation plan modeling, advanced practice provider leverage, incident-to and split/shared billing review, and template and access analysis
- Partner with customer delivery to understand our customer’s medical group operations, identify pain points, and translate complex financial and clinical requirements into technical solutions
WHAT YOU HAVE
- 7+ years in physician enterprise, medical group operations, practice administration, or an equivalent function, ideally with exposure to compensation design, productivity benchmarking, and access management
- Strong proficiency with practice management and scheduling systems (Epic Cadence or similar), compensation modeling in spreadsheets, and benchmark sources such as MGMA, including a working understanding of where benchmark comparisons mislead
- 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
- Demonstrated ability to deliver high-quality analyses on demanding deadlines
- Ability to effectively communicate with a variety of internal and external stakeholders, including physicians, and translate complex problems between medical group operations, 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 physician economics and AI
- Willingness and desire to do work in the trenches — e.g., grading model-generated productivity findings provider by provider, auditing encounters for workflow analysis, separating a real productivity gap from a coding-behavior artifact by hand. Getting AI to reason about physician behavior requires a lot of reasoning about physician behavior
NICE TO HAVE
- Experience at a high-growth startup, or across both employed-group and independent network models
- 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