Director of Engineering, AI Platform

We're looking for a Director of Engineering to lead our Applied AI & Data Platform organization, the group turning today's best models into reliable agents that do real administrative work, plus the data systems behind them. You'll report to the CTO and lead a growing org of engineering managers, staff engineers, ML engineers, and data specialists. You'll work with Product, Operations, and our domain experts to decide where AI creates lasting advantage for Medallion.

You'll scale what's already working. We've built the point solutions: LLM inference and model lifecycle, voice agents, document and data-extraction pipelines, early evals. Your job is to turn them into a platform, with shared tooling, rigorous evaluation, scalable data ops, and a repeatable way to ship new agents safely.

We want someone who's here to build an AI practice, driving real outcomes, not bolt features onto a product.

• Own the AI roadmap. Set direction across our AI surfaces: voice and computer-use agents that work payers and provider portals, email triage, and the models that extract and structure data from clinical and legal documents. The build-vs-buy and fine-tune-vs-prompt calls are yours.
• Push AI deeper. Take our systems from assisting people, to making decisions, to running whole service requests, and into licensing, enrollment, monitoring, and the customer-facing product.
• Make it production-grade. Build the evals, observability, and human-in-the-loop systems that let us ship models and agents we'll stand behind and back with guarantees.
• Lead the data platform. Own the pipelines, datasets, and tooling behind the models. Our real-world data is our biggest advantage, so make it fast to use and trustworthy to build on.
• Build the org. Hire and grow managers and senior engineers, set the structure, and hold the bar as we scale. Stay close enough to the work to earn their trust in a design review.

• Manager of managers. 10+ years across engineering, ML, or data science, with experience leading a multi-team org through other managers.
• Breadth across functions. You've run a mix of disciplines, including engineering, ML, data science, and data labeling or human-in-the-loop operations, rather than a single eng team.
• Shipped ML/AI at scale. You've put applied ML and LLM systems into production, with the teams to back them, well beyond prototypes and research.
• Hands-on AI depth. You understand LLMs, agents, retrieval, and structured extraction well enough to set direction, and you know what production reliability actually takes.
• Data platform strength. You're grounded in the data and infrastructure side: pipelines, orchestration, inference, and the eval and observability tooling that supports ML.
• Owns the outcome. You weigh speed, cost, quality, and risk, and you take accuracy seriously where mistakes have real consequences.

The healthcare back office is about to be reinvented, and we're the ones doing it. We started with licensing, credentialing, and enrollment, but the ambition is bigger: every workflow we automate earns the right to take on the next, until we run the back office end to end. It's the kind of problem applied AI is finally good enough for, and we're positioned to do it. 300+ organizations run on Medallion today, which gives us proprietary data, verifiable ground truth, and tight production feedback loops, plus the domain expertise and funding to go after it properly.

Getting there means applying ML and AI across more of the back office, a data platform that makes those systems reliable, and the team to build it all.