Founding Applied AI Lead

About the role

Paxos Health is looking for a hands-on Founding Applied AI Lead. You'll work directly with the founders and help define the applied AI function at a seed-stage company before the playbook exists. (Note that the job title itself is flexible based on candidate preferences.) The challenge is turning probabilistic LLM behavior into reliable, auditable workflows that customers trust in production. We've succeeded with the customers we've had so far, and we want to scale our AI to many more. This role is deeply hands-on, but the core output is not production backend code. It is the written and structured system design layer around our AI: precise agent instructions, workflow specs, output schemas, implementation playbooks, and decisions about how messy customer processes should be translated into reliable product behavior. As an early employee, there is a significant opportunity to grow the scope of your role. For example, you could become more product-adjacent, or more engineering-adjacent, depending on your interests. (See the AI-generated diagram directly below.) We are open to many backgrounds for this role, not just software engineering. For example, a strong senior technical writer can be a great fit for this role. Much of the work is about precise instructions, structured outputs, reusable specs, and customer-facing implementation logic. If you have owned complex documentation systems, API or developer docs, information architecture, docs-as-code workflows, or cross-functional technical content, this role applies those same skills to AI systems in production.

Responsibilities

• You will own the applied-AI layer of Paxos's customer workflows. In practice, your work will include:
• Designing AI workflows from messy real-world processes: translate customer reimbursement workflows into clear system behavior, including inputs, outputs, decision rules, edge cases, escalation paths, and human review steps.
• Writing and improving agent instructions: create prompt/instruction sets that help agents extract clinical facts, apply payer policies, cite evidence, identify missing information, and generate reviewer-ready prior authorization or appeal content.
• Creating reusable implementation playbooks: turn customer-specific learnings into clear internal specs, templates, QA checklists, reviewer guidance, and implementation patterns that help Paxos scale beyond one-off customer builds.
• Defining structured outputs: design schemas, evidence tables, decision trees, missing-information checklists, and reviewer-facing summaries that make AI outputs reliable, auditable, and easy to evaluate.
• Building evals and QA processes: create test cases, eval sets, quality rubrics, and failure-review loops so we can measure and improve workflow performance over time.
• Debugging AI workflow failures: inspect outputs, identify why an agent missed evidence or followed the wrong logic, and turn those findings into improvements to prompts, schemas, workflow design, or product behavior.
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Benefits

Additional Information

This job post is for our NYC location. For our Toronto location job post, go here . We are flexible on the precise job title. It could include "forward deployed," "engineer," "technical writer," "implementation lead," etc. For example, if you're a technical writer and want to keep that job title, we can accommodate that. Paxos Health is a Seed-stage healthcare AI startup that has raised >$6M in venture capital funding, with AI agents already operating in production with customers. Our founding team comes from Stanford, Meta, Microsoft, Medtronic, Columbia, University of Waterloo, and has founded previous startups and healthcare nonprofits. We are building the AI-native operating system for getting patients access to 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, and breakthrough technologies struggle to reach the people they were built to help. We help healthcare manufacturers fight for coverage patient-by-patient. 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 Raised >$6M 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. So far, we've deployed LLMs for written documentation workflows and voice AI, and we want to expand those and build out computer-use agents as well.

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