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Latent builds clinical intelligence software that helps health systems manage complex pharmacy operations, improving patient access to medications and enabling care teams to focus on patient care. The platform combines healthcare workflows, enterprise software, and applied AI.
As Head of Applied AI, you will lead the team responsible for building and shipping production AI capabilities across Latent's product suite. You will own the strategic direction and execution of applied AI across clinical and operational workflows including prior authorization, payor communication, clinical review, and care gap analysis.
Key responsibilities include:
- Setting strategy and operating model for Applied AI across the organization
- Leading the team building production AI agents and intelligence systems for clinical workflows
- Owning Latent Agent (the company's agent harness), evaluation frameworks, and shared foundations for new AI use cases
- Establishing evaluation practices that drive product, technical, and business decisions
- Making pragmatic build-versus-buy decisions for models, prompting, agent infrastructure, and post-training approaches
- Partnering with Product, Engineering, and business leaders to define priorities, metrics, and ownership
- Guiding technical direction while empowering technical leads and senior engineers
- Building, hiring, and developing a high-performing team with clear expectations and regular feedback
- Owning reliability of live AI systems, including monitoring, failure handling, and SLOs
- Communicating reasoning clearly, including options considered, confidence levels, and decision drivers
The role is both technical and organizational leadership. You will move between strategy and technical detail, review design documents closely, and provide concrete guidance on architecture and tradeoffs. The company operates with small teams by design, where everyone understands customer problems, owns decisions, and follows work through to measurable production outcomes.
REQUIREMENTS:
- Shipped production-grade LLM agents (not prototypes or research systems only)
- Owned live production systems with on-call responsibilities, reliability ownership, and SLO accountability
- Trained or fine-tuned models, shipped them, and measured impact against product or business outcomes
- Strong judgment about when to post-train models, prompt engineer, improve agent harness, or use off-the-shelf capabilities
- Led teams that shipped products, either as a manager or as a staff-level engineer with significant leadership responsibility
- Ability to move between strategy and technical detail, read design documents closely, and offer concrete guidance on architecture and tradeoffs
- Experience partnering 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
- Clear communication of reasoning, including options considered, confidence, and decision drivers
- Healthcare experience or experience building systems using sensitive data is helpful but not required
