AI Engineer

Develop Health is on a mission to use AI to radically accelerate access to life-saving medications. By automating complex, manual healthcare processes—like benefit verification and prior authorization—we’ve grown from $0 to >$10M in annual recurring revenue in less than 2 years, and currently help more than 400,000 new patients every month.

We’re partnering with some of the largest pharmacy benefit managers and payers in the nation, revolutionizing how healthcare interactions occur by eliminating human delays and inefficiencies. Our small, elite team of founders and engineers have previously launched and exited successful healthcare startups including Rupa Health and Canvas Medical. We are now scaling rapidly following a major funding round.

We’re hiring an AI Engineer to take models from prototype to production and drive real clinical impact through applied AI. You’ll have significant autonomy to define high‑value projects, build production‑ready systems, and ensure our models are safe, reliable, and deeply useful in the healthcare workflow.

You’ll work closely with our product engineers, clinical leaders, and CTO to ensure that our AI delivers measurable improvements in patient access and operational efficiency. This is a hands‑on role that combines cutting‑edge AI development with pragmatic engineering and evaluation.

• Build and curate datasets and context sources to enable exceptional prompt engineering and evaluation of frontier LLMs, optimizing inputs, retrieval, and context to deliver cutting‑edge performance and reliability.

• Design and maintain evaluation pipelines that rigorously measure model accuracy, reliability, and real‑world clinical value.

• Prompt, test, and iterate on LLM workflows—including retrieval, agent logic, and tool use—to achieve industry‑leading performance.

• Deploy models into production: design APIs, integrate inference into our Python‑based backend, and implement observability and monitoring.

• Stay on the frontier of AI research, evaluating and incorporating advancements that enhance our platform.

• Develop and scale systems for continuous model improvement and learning, informed by real production feedback.

• Build AI infrastructure and tooling to enable other teams to safely prototype and deploy models.

• Collaborate with engineering leadership to architect the next‑generation AI platform that supports 100× transaction volume.

• Advance the AI‑driven features of our product—making our automation smarter, faster, and more context‑aware.

• Mentor future AI hires and help shape the company’s applied AI roadmap.

• 3+ years of experience in applied AI or machine learning engineering.

• Deep, hands‑on experience with LLMs, modern AI frameworks, and evaluation techniques.

• Strong proficiency in Python, including API design and integration of AI models into production systems.

• Demonstrated ability to independently identify impactful projects and deliver them end‑to‑end.

• A mindset oriented toward ownership, autonomy, and measurable outcomes, not ticket‑driven work.

• Comfort operating in a fast‑paced, high‑autonomy startup environment.

• Availability to work on‑site in Menlo Park at least three days per week.

• How our AI systems integrate with benefit verification and prior authorization workflows, and develop a deep understanding of the healthcare ecosystem—how data moves through payers, providers, and patients, and how our automation fits into these processes.

• Our internal data sources, operational rhythms, and model evaluation standards.

• Emerging AI techniques relevant to retrieval‑augmented generation, fine‑tuning, and evaluation automation.

• Experience in healthcare or health‑tech environments.

• Prior startup or founding‑team experience.

• Familiarity with observability and monitoring frameworks for production ML systems.

• Contributions to open‑source AI projects or applied research.

• Competitive salary + meaningful equity in a company growing 10× year‑over‑year.

• Health, dental, and vision coverage; flexible PTO.

• High‑end workstation & tooling budget.

• The opportunity to build AI that directly improves access to care for millions of patients.