Base Salary
$200k - $285k/yr
Responsibilities
- Design and build high-performance, reliable systems that process and serve large-scale clinical data.
- Build and operate systems handling high-volume asynchronous workloads using Kafka and Temporal.
- Build and maintain integrations with Electronic Health Records and other external systems.
- Implement complex workflows involving Prior Authorizations and Patient Interactions.
- Integrate and operationalize in-house clinical AI models and external models in production systems.
- Contribute across APIs and frontend layers to ship complete user-facing features when needed.
Requirements
- Strong experience building backend systems in TypeScript.
- Experience with distributed systems and data infrastructure such as PostgreSQL, Redis, Kafka, Temporal, or similar technologies.
- Proven ability to design systems and manage complexity in production environments.
- Experience with Kubernetes and infrastructure tooling such as Helm is a bonus.
Benefits
- In-person work five days per week in the San Francisco office.
Tech Stack
Categories
About Latent
Latent is the leading Enterprise Pharmacy Intelligence platform, powered by a clinical agentic engine enables health systems to serve more patients, grow their pharmacy operation, and capture more value compliantly, without adding headcount. The Latent platform spans the full pharmacy stack: prior authorization, appeals, denials, capture, outreach, and adherence. It frees pharmacy teams from the administrative work that fills the day, enabling them to focus on their patients and practice at the top of their license. Latent is trusted by 50+ of the largest healthcare systems in the country including Ochsner Health, St. Luke's Health System, MetroHealth, Yale New Haven Health, Mount Sinai Health System, Vanderbilt University Medical Center, UCLA Health, UCSF Health, Henry Ford Health and many more. Latent makes pharmacy the growth and care delivery engine for modern healthcare systems by enabling them to simplify the journey of their patients from diagnoses to health.