Joyful Health is a healthcare AI financial infrastructure company that connects fragmented revenue cycle systems into a single source of financial truth for providers.
Building the financial operating system for healthcare, and bringing the joy back to healthcare by fixing the financial chaos behind it. The healthcare payment system is a complex and inefficient maze - healthcare practices leave $125 billion in revenue uncollected each year, lost in the chaos of fragmented financial data, manual workflows, and opaque payer systems. This financial uncertainty leaves practices struggling to stay afloat, while valuable revenue slips through the cracks. Joyful Health is building the AI-powered financial operating system for healthcare practices. Our mission is to bring the joy back to running a private practice by simplifying financial operations so providers can focus on patient care. We spent 10 months working as fractional CFOs for a dozen practices, doing this work side by side with providers as we developed our product. We just announced our $22M Series A led by CRV and world-class investors including the founders of MongoDB & KAYAK. 🚀 We have an enormous opportunity in front of us. The broken healthcare payment ecosystem impacts practices of all sizes, and the opportunity to make a real difference is massive. This role is full-time. We’re looking for someone NYC-based who is open to coming into the office 3 days a week. The base pay range for this role is $150,000–$275,000 per year. ROLE DESCRIPTION We're looking for a talented and ambitious machine learning engineer with 5+ years of experience building production AI systems who's excited to solve one of the toughest challenges in healthcare - automatically resolving claim denials. You'll be the technical lead for our agentic AI platform that autonomously researches, understands, and resolves insurance claim denials, helping healthcare practices recover millions in lost revenue. Insurance claim denials cost healthcare practices $125 billion annually, and resolving them requires deep expertise in medical billing, payer rules, and claim documentation. Today, billing managers spend hours manually researching each denial, gathering supporting documentation, and crafting appeals. We're building an agentic AI system that automates this entire workflow - from denial classification and root cause analysis, to evidence gathering and appeal letter generation. As a machine learning engineer, you'll architect and build the intelligent agents that power this system. You'll design multi-agent workflows that collaborate to interpret complex denial reasons, retrieve relevant clinical and billing information through RAG systems, reason about payer policies, and generate compelling appeals. This is a greenfield opportunity to build a production agentic system that will directly impact the financial health of healthcare practices. You should be comfortable (and excited!) about working in a fast-paced, early-stage startup environment where you'll be able to wear many hats and take on new challenges as they arise. This is a great opportunity for someone who wants to work on something extremely challenging at the intersection of AI and healthcare. This role is full-time. We're looking for candidates based in NYC. WHAT YOU'LL DO - Design and build the architecture for our agentic AI system that autonomously resolves insurance claim denials - Develop specialized AI agents for denial classification, root cause analysis, evidence retrieval, policy reasoning, and appeal generation - Implement multi-agent orchestration frameworks that coordinate complex workflows across research, decision-making, and document generation - Build and optimize RAG systems to retrieve relevant clinical documentation, billing records, and payer policy information - Create evaluation frameworks and feedback loops to continuously improve agent performance and reliability - Design prompt engineering strategies and fine-tuning approaches to optimize LLM behavior for healthcare billing workflows - Work closely with billing managers to understand denial resolution workflows and translate them into agent behaviors - Collaborate with the engineering team on production infrastructure for deploying and monitoring AI agents at scale - Actively contribute to building the team's AI/ML vision and technical roadmap - Collaborate on code reviews and technical design documents to ensure code quality and distribute knowledge WE’D LOVE TO HEAR FROM YOU IF… - You’re passionate about Joyful Health’s mission – you want to solve real, pressing problems for providers and put them in control of their businesses. You naturally empathize with users and enjoy designing solutions with their needs in mind. - You’re excited to shape the future of our engineering team and are excited to lead by example – as an early team member, you’ll have the chance to influence everything from our technical roadmap to our company culture. You have a radical sense of ownership with the ability and desire to own technical roadmaps and refine ambiguous problems. You also have a low ego, growth mindset, and desire to see the product and team scale with your impact. - You have a love for your craft and strong technical execution - you view coding as a craft, not just a task, and you're excited about using technology to create meaningful impact. Your technical skills go beyond coding—you care about building a product that works at scale, with the user at the forefront. We care more about your drive, hustle, and passion than a college degree from a shiny institution or experience at name-brand companies. - You have 5+ years of experience in machine learning engineering, with a focus on building production AI systems and deploying models at scale. - Strong experience with machine learning frameworks (PyTorch, TensorFlow, or JAX) - Experience working with healthcare data or understanding of medical billing workflows is a plus - Proficiency in: - Python and modern ML frameworks (PyTorch, TensorFlow, or JAX)