Head of Applied AI
Latent is hiring a Head of Applied AI in San Francisco, United States. It pays $285k-$300k a year and Level rates it ; you can apply on Level.
AI in this role
About Latent
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.
The role
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.
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-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
Experience we value
You have built and shipped production-grade LLM agents, not only prototypes or research systems
You have owned live production systems, including on-call responsibilities, reliability, and service-level objectives
You have trained or fine-tuned a model, shipped it, and measured its impact against product or business outcomes
You have strong judgment about when to post-train a model, prompt engineer, improve the agent harness, or use an off-the-shelf capability
You have led teams that shipped products, either as a manager or as a staff-level engineer with significant leadership responsibility
You can move between strategy and technical detail, read design documents closely, and offer concrete guidance on architecture and tradeoffs
You have partnered closely with Product and stakeholders outside engineering to deliver business outcomes
You have built AI for domains where deep context, complex requirements, or operational constraints define the product
You have made difficult performance and organizational decisions with care and clarity
You communicate your reasoning clearly, including the options you considered, your confidence, and what would change your mind
Healthcare experience, or experience building systems that use sensitive data, is helpful but not required.
How we work
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.
How we rate this
Head of Applied AI at Latent rates 66 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Describe a typical day in a role like this one: which parts run through AI directly?
- If you removed AI from this role, what would be left, and how do you decide what still needs a human?
Adapt your resume
- List these exact terms on your resume: AI agents. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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