Head of Applied AI

About the role

Latent builds clinical intelligence that helps health systems run complex pharmacy operations, giving patients access to the medications they need and helping care teams focus on serving patients. The work spans healthcare workflows, enterprise software, and applied AI. Engineers work closely with Product and customer-facing teams to understand the problem, build the solution, and own the outcome in production. We are hiring a Head of Applied AI to lead the team building the AI capabilities that power Latent’s products. You will own our approach to production AI across complex clinical and operational workflows, including prior authorization, payor communication, clinical review, and care gap analysis, etc. You will lead the development of Latent Agent, our agent harness, as well as the evaluation systems and shared foundations that help teams build reliable clinical AI products. You will decide when to use off-the-shelf models, invest in post-training, or take on research bets where our data and health-system relationships create an advantage. This is both a technical and organizational leadership role. You will set strategy, guide architecture, build and develop the team, and work closely with Product, Engineering, and business leaders to turn technical capabilities into measurable outcomes for health systems and patients. Our teams are small by design. Everyone is expected to understand the customer problem, own their decisions, and follow their work through to measurable outcomes in production. We learn by shipping, measuring, and improving. We treat evaluations as a core part of product development. We invest in models when the product and business need them, and we stay pragmatic about using the simplest approach that works reliably.

Responsibilities

• Set the strategy and operating model for Applied AI at Latent
• Lead the team building production AI agents and intelligence systems for clinical and operational workflows
• Own Latent Agent, our evaluation framework, and the shared foundations for new AI use cases
• Establish evaluation practices that drive product, technical, and business decisions
• Make pragmatic build-