Base Salary
$225k - $300k/yr
Responsibilities
- Own the architecture, data, modeling, evaluation, and production infrastructure for end-to-end ML systems.
- Train and fine-tune large language models for clinical reasoning, medical question answering, and evidence-grounded generation.
- Make tradeoffs across accuracy, latency, cost, and safety in high-stakes production environments.
- Develop evaluation frameworks for model safety and clinical validity.
- Integrate ML systems into product workflows and patient-facing applications.
- Monitor production performance and iterate using real-world usage and feedback.
- Define correctness for ambiguous clinical workflows in collaboration with engineers and clinicians.
- Drive systems from ambiguous problem definition through reliable production deployment and set technical direction.
Requirements
- Strong foundation in machine learning and software engineering.
- Track record of building and owning production ML systems where performance, reliability, or correctness materially mattered.
- Experience driving ambiguous ML problems from problem formulation and model design through productionization.
- Hands-on experience with PyTorch or similar frameworks.
- Ability to operate independently in high-ambiguity environments with minimal guidance.
- Strong product and engineering judgment, including knowing when to use ML and how to scope problems.
- Comfort working in a fast-moving, early-stage environment.
- Experience with systems whose decisions have real-world consequences, such as healthcare, finance, or infrastructure.
- Experience deploying LLMs in production is preferred.
- Experience building distributed systems or large-scale data pipelines is preferred.
- Experience working with clinical, biomedical, or other regulated datasets is preferred.
Benefits
- Work in person in San Francisco, spending most of the week in the office.
- Competitive compensation and meaningful equity.
- Significant ownership in a small, high-caliber team.
- Work on high-stakes systems with direct impact on patient care.
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.