Head of Applied AI

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. We are hiring a Head of Applied AI to lead the team building the AI capabilities that power Latent's products. You will own the approach to production AI across complex clinical and operational workflows, including prior authorization, payor communication, clinical review, and care gap analysis. You will lead the development of Latent Agent, the agent harness, as well as the evaluation systems and shared foundations that help teams build reliable clinical AI products. This is both a technical and organizational leadership role, working 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, and everyone is expected to understand the customer problem, own their decisions, and follow their work through to measurable outcomes in production.

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, the evaluation framework, and the shared foundations for new AI use cases; Establish evaluation practices that drive product, technical, and business decisions; Make pragmatic build-versus-buy decisions across models, prompting, agent infrastructure, and post-training; Partner with Product, Engineering, and business leaders to define priorities, metrics, and ownership; Guide technical direction while empowering technical leads and senior engineers to own execution; Build, hire, and develop a high-performing team with clear expectations and regular feedback; Create predictable, visible execution without adding process for its own sake; Own the reliability of live AI systems, including monitoring, failure handling, and service-level objectives

Requirements

Built and shipped production-grade LLM agents, not only prototypes or research systems; Owned live production systems, including on-call responsibilities, reliability, and service-level objectives; Trained or fine-tuned a model, shipped it, and measured its impact against product or business outcomes; Strong judgment about when to post-train a model, prompt engineer, improve the agent harness, or use an off-the-shelf capability; Led teams that shipped products, either as a manager or as a staff-level engineer with significant leadership responsibility; Able to move between strategy and technical detail, read design documents closely, and offer concrete guidance on architecture and tradeoffs; Partnered closely with Product and stakeholders outside engineering to deliver business outcomes; Built AI for domains where deep context, complex requirements, or operational constraints define the product; Made difficult performance and organizational decisions with care and clarity; Communicates reasoning clearly, including options considered, confidence, and what would change their mind

Nice to have

Healthcare experience; Experience building systems that use sensitive data