Founding AI Workflow Architect

This job post is for our NYC location. For our Toronto location job post, go here. Paxos Health is on a mission to help patients access the medical technologies their doctors prescribe. Too often, even when a physician decides a patient needs a healthcare product (cancer tests, genetic diagnostics, prosthetics, heart valves, neurostimulation devices, home medical equipment, etc.), health insurance companies deny coverage because the evidence, policy, and documentation requirements are complex and inconsistent. Patients don’t get treated, and breakthrough technologies struggle to reach the people they were built to help. We help healthcare manufacturers fight for coverage patient-by-patient. Paxos uses LLMs to turn messy clinical records, payer rules, PDFs, portals, faxes, and reimbursement logic into rigorous prior authorizations and appeals to get patients covered. Before LLMs, this work was too difficult to use AI technology for, and human teams were forced to cut corners to get through all of the patient cases. But now, AI can help manufacturers meet increasingly complex insurer requirements and get more patients access to care. Our progress so far: Five medtech and lab diagnostics companies as customers, with 40+ companies in pipeline $182K in recognized revenue, with line of sight to $1M ARR by late summer 9 of our last 10 demo calls came through referrals, word of mouth, or thought leadership Raised >$5M across two VC rounds, including our recently closed Seed Won the Stanford Impact Founder and IDIF fellowships. Check out the Stanford article about us and Haley’s LOWkeynotes talk. About the role Paxos Health is looking for a hands-on Founding Applied AI Lead to design and own the AI agents and workflows that make this possible. (Note that the job title itself is flexible based on candidate preferences.) You’ll sit at the intersection of product, engineering, AI, and customer implementation. You’ll translate messy manual workflows into reliable AI systems that are used in the real world, which includes prompt chains, structured-output schemas, evals, workflow specs, and implementation logic. This is a technical workflow-building role. You should be comfortable with, or able to quickly learn, technical building blocks like JSON, structured outputs, low-code workflow tools, and lightweight scripting. You won’t need to write production code, but you should be able to prototype, test, and debug AI workflows. As an early employee, there is a significant opportunity to grow the scope of your role as our company expands. Responsibilities Design and improve Paxos’s AI workflows. Own the prompt chains, agent patterns, structured-output schemas, evals, and test cases that power customer implementations. Turn reimbursement complexity into testable workflow logic. Translate payer rules, document requirements, clinical evidence standards, and operational edge cases into clear AI system behavior. Own AI quality and reliability. Analyze failures, improve prompts and schemas, and build evals that catch issues before they reach production. Work directly with customers during implementation. Clarify requirements, understand edge cases, and translate what we learn into workflows and reusable Paxos capabilities. Document how the system works. Write workflow specs, test plans, prompt/eval documentation, and implementation notes that help the team build and improve faster. For example, you might turn a payer policy into a structured decision tree, design an eval set for appeal quality, create a reusable prompt pattern for extracting clinical evidence from records, debug why an AI workflow failed on a customer case, and write the implementation spec that engineering and ops use to deploy it. What this role is not: This is not a people-management role at the outset. You’ll be hands-on and work closely with the whole team. This is not a 100% customer-facing role. You’ll work with customers, but much of your time will be spent optimizing the AI workflows and working with our team. The split will be ~75% AI workflow ownership and ~25% customer-facing implementation. This is not a pure software engineering role. (If the person we hire has a software engineering background, they can write some production code, but coding will not be a majority of the role.) This is not an AI research role. You’ll apply LLMs to real workflows, not train models or write research papers. This is not a large-company role with narrow responsibilities. You’ll work in a fast-moving startup where ownership matters more than rigid job boundaries. Requirements We also welcome candidates with many more years of experience than these minimum requirements, and the role can scale up in scope and higher within the compensation range. Minimum 2+ years of work experience in any one of the following: technical implementation, AI operations, software technical writing, software engineering, technical customer success, or related work. AI-obsessed: you actively experiment with LLMs, and you believe AI will reshape how real-world operations work. Communication skills: strong English written and verbal communication. Extreme detail-orientation: you notice edge cases, inconsistencies, missing requirements, and quality issues that others overlook. Customer-facing: comfortable interacting directly with enterprise customers. High ownership: comfortable operating in a fast-moving startup environment where processes are still being built. Based near New York City or Toronto, or willing to relocate. This is an in-person / hybrid (minimum 3 days per week in the office) role with the Paxos team. Nice to have None of the below qualifications are strictly required. You have built LLM agents, automations, prompt chains, or AI workflows for work, side projects, or your own productivity. You have experience writing technical specs, implementation docs, workflow documentation, test plans, or customer-facing technical materials. You have worked with structured data, JSON, APIs, low-code tools, workflow builders, or lightweight scripting. You have designed or maintained evals, QA processes, test cases, or other systems for improving AI reliability. You have worked at an early-stage startup, started your own company/project, or operated in a high-ownership environment with lightweight structure and processes. You have worked on complex, document-heavy workflows in a regulated or high-stakes domain, such as healthcare, legal, fintech, insurance, or compliance. You have experience in healthcare, healthcare operations, reimbursement, prior authorizations, appeals, RCM, insurance, or clinical documentation. About the team More on us here. Haley King , CEO & Co-Founder: Haley spent seven years at Medtronic across engineering, product strategy, FDA/regulatory work, M&A, and commercialization. She holds an MBA from Stanford. Alex Lacey , CPO & Co-Founder: Alex was a product manager at Meta and Microsoft, and he has been a serial entrepreneur since high school. His interest in Paxos is also personal: after fighting and winning his own insurance denial, he became focused on using AI to make access easier for patients. He also holds an MBA from Stanford. Malcolm Asher , Head of Ops & Co-Founder: Malcolm has spent years working on patient access, including founding a global youth-led healthcare nonprofit and later helping patients write appeals after winning his own insurance denial. He’s a graduate of Stanford University. Ben Setel , Founding/Lead Engineer: Ben leads Paxos’s engineering efforts and brings a rare mix of software engineering, AI, startup, legal, and compliance experience. Before Paxos, he founded a machine learning startup and worked in cybersecurity and data privacy law as an attorney. Vikram Karuna , Founding Engineer: Vikram is a senior software engineer with 15+ years of experience, including at Microsoft, and he founded an AI-powered scuba platform with 5,000+ users. He studied at the University of Waterloo. Vibes: You can expect a fast-paced, intense, dynamic, and exciting working environment where everyone has strategic ownership. We take our work seriously, but we don’t take ourselves seriously, and we are building a culture that prioritizes constant learning and feedback. We also play online games like Geoguessr at the end of every standup. Compensation $110,000–$175,000 USD base salary, calibrated to candidate level Very generous equity package We offer health, dental, and vision insurance, plus standard paid time off per early-stage startup norms #J-18808-Ljbffr Pear VC