[Remote] Director of Engineering, AI Platform
Note: The job is a remote job and is open to candidates in USA. Medallion is a leading provider operations platform focused on improving healthcare delivery by automating administrative tasks. They are seeking a Director of Engineering to lead their Applied AI & Data Platform organization, responsible for building reliable AI agents and data systems that enhance operational efficiency in healthcare.
Responsibilities
- 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
Skills
- 10+ years across engineering, ML, or data science, with experience leading a multi-team org through other managers
- 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
- You've put applied ML and LLM systems into production, with the teams to back them, well beyond prototypes and research
- You understand LLMs, agents, retrieval, and structured extraction well enough to set direction, and you know what production reliability actually takes
- You're grounded in the data and infrastructure side: pipelines, orchestration, inference, and the eval and observability tooling that supports ML
- You weigh speed, cost, quality, and risk, and you take accuracy seriously where mistakes have real consequences
Company Overview
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