--- title: 'Head of Applied AI at Latent' canonical: 'https://feeny.ai/job/head-of-applied-ai-latent-san-francisco-3f9t99je7z9b' type: 'job' last_seen: '2026-10-19' --- # Head of Applied AI at Latent - **Company:** Latent - **Location:** San Francisco, CA - **Employment:** full-time - **Work type:** onsite - **Posted:** 2026-10-12 - **Last confirmed live:** 2026-10-19 - **Apply:** https://jobs.ashbyhq.com/latent/135e2c49-440b-496d-92aa-541143b252f3 ## Job description Head of Applied AI ## 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. ## About Latent ## Company Overview - **One-liner**: Latent provides an enterprise pharmacy intelligence platform that uses a clinical AI engine to automate medication access workflows—such as prior authorizations and appeals—for health systems. - **Entity Type**: Private (Series A) - **Headquarters**: San Francisco, California, United States - **Founded**: 2022 - **Founders**: Rishabh Jain and Sriram Somasundaram ## Core Business - **Primary industry/industries**: Healthcare IT, AI-powered clinical operations, pharmacy intelligence - **Target customers**: B2B; large health systems (hospitals and health networks) – currently serving over 45 partners including 50% of the top 20 U.S. health systems. - **Mission or purpose statement**: “Closing the distance between a therapeutic decision and a patient receiving therapy” by automating the manual knowledge work that sits between a doctor’s order and treatment. ## Products & Services - **Clinical Reasoning Engine**: An AI platform that performs clinical knowledge work—reasoning through patient data, interpreting drug criteria, extracting evidence, and orchestrating workflows across EHR, payers, pharmacies, and patients. Automates prior authorization, payer policy parsing, form auto-filling, and medical necessity checks. - **Pharmacy Intelligence Platform**: A suite that centralizes pharmacy operations, increases prior-authorization throughput, reduces denials, and helps pharmacists focus on patients instead of paperwork. ## Market Standing - **Valuation/Market Cap**: Not publicly available - **Key Metric**: Total funding raised – $80.5 million (Series A as of April 2026) - **Notable Investors/Partners**: Spark Capital, Transformation Capital, McKesson Ventures, Conviction, General Catalyst, Y Combinator - **Growth Signals**: - Grew from 4 to more than 45 health system partners in the past year (2025–2026) - Now serves 2 million patients annually - Reduces denials by over 30% and enables clinicians to serve twice as many patients - Customers include Yale New Haven Health, Ochsner Health, MetroHealth, UCI, Vanderbilt Health, Mount Sinai, Henry Ford Health, UCSF Health, UCLA Health ## Competitive Advantages - Deep, native integration with any EHR; the engine operates directly within the patient record. - Proprietary clinical agentic engine trained on medical reasoning, payer rules, and drug criteria – a high bar for regulatory and clinical accuracy. - Proven operational impact: 109% more prior-auth throughput per person, 23.4 minutes of every appeal handed back to pharmacists, and $300K+ annual labor cost savings cited by customers. - Already embedded in 50% of the top 20 U.S. health systems, creating a high switching cost. ## Strategic Focus - Expand health system footprint and deepen the platform connecting hospitals, payers, pharmacies, and patients. - Continue investing in reliability and trust required for healthcare deployment. - Scale the team and apply the Clinical Reasoning Engine proactively to identify patients who should start therapy and keep them on treatment. - Move beyond prior authorization into every process where clinical knowledge must be translated into action. ## Why Work Here - **Culture**: High agency, ownership, and independence; engineers own projects from ideation to completion. Values include bias to action, practical innovation, and customer obsession (regularly visiting partner sites). - **Remote/Hybrid**: Remote-friendly (headquarters in San Francisco; team is distributed). - **Notable perks/engineering culture**: Fast-moving environment with ambitious product and GTM goals; opportunity to work on one of healthcare’s hardest problems using modern AI/ML; team size ~50 (as of early 2026) – still a growth-stage startup. ## Sources 1. [latenthealth.com](https://latenthealth.com/) 2. [latenthealth.com/about](https://latenthealth.com/about) 3. [latenthealth.com/careers](https://latenthealth.com/careers) 4. [ycombinator.com/companies/latent](https://www.ycombinator.com/companies/latent) 5. [cbinsights.com/company/latent](https://www.cbinsights.com/company/latent) ## Other roles at Latent - [Security Engineer](https://feeny.ai/job/security-engineer-latent-san-francisco-or-new-york-city-58bxqak9zgpe) — San Francisco OR New York City, OR - [Director, 340B Solutions and Partnerships](https://feeny.ai/job/director-340b-solutions-and-partnerships-latent-san-francisco-or-new-york-city-fqkdxhw0pstv) — San Francisco OR New York City, OR - [Product Marketing Lead](https://feeny.ai/job/product-marketing-lead-latent-new-york-jjbew82xm38m) — New York, NY - [Strategic Account Executive (San Francisco)](https://feeny.ai/job/strategic-account-executive-san-francisco-latent-san-francisco-p6dzkeh3qcqf) — San Francisco, CA - [Strategic Account Executive (New York City)](https://feeny.ai/job/strategic-account-executive-new-york-city-latent-new-york-ghzbpwxwwzh0) — New York, NY - [Implementation Project Manager](https://feeny.ai/job/implementation-project-manager-latent-remote-y4j1g0bskvax) - [Head of Partner Enablement](https://feeny.ai/job/head-of-partner-enablement-latent-san-francisco-or-new-york-city-6xnve4vb02zy) — San Francisco OR New York City, OR - [Clinical GTM Lead](https://feeny.ai/job/clinical-gtm-lead-latent-remote-8q7mnaxdbpxr) - [Associate Executive Business Partner (New York)](https://feeny.ai/job/associate-executive-business-partner-new-york-latent-new-york-8jdt7y8z3jjf) — New York, NY - [Account Manager - Health Systems (New York City)](https://feeny.ai/job/account-manager-health-systems-new-york-city-latent-new-york-h5e16xn81xg4) — New York, NY