Machine Learning Engineer

Job description

# **Machine Learning Engineer** ## **About Latent Health** Healthcare today is only truly **personalized** for two groups: those with wealth and access, and those with physicians in their immediate family. For everyone else, care is fragmented and impersonal. Medical history is scattered across systems that don’t communicate. Physicians have minutes to understand decades of context. And when something goes wrong, patients are left with tools that understand medicine broadly—but not the individual. We believe this can be fundamentally rebuilt. At Latent Health, we are building systems that understand both: - the **population** (clinical knowledge at scale) - and the **individual** (longitudinal patient history) Our models are designed to answer complex clinical questions with patient-specific context and verifiable reasoning. Our dataset represents one of the most clinically diverse populations in the United States, including patients with chronic illness and complex disease. Each patient record contains extraordinary depth. ## **ML at Latent Health** The Machine Learning team is responsible for building systems that run in real clinical workflows. We work on: - Verifiable reinforcement learning at scale - Mid-training and post-training of foundation models - Novel objectives derived from longitudinal patient data We are a small group of researchers and engineers focused on pushing the frontier while shipping real systems into production. We are a small team and expect engineers to take ownership of critical systems, not components. ## **The Role** As a Machine Learning Engineer, you will **own the design, development, and operation of production-grade ML systems** that run in real clinical workflows. You will drive systems from ambiguous problem definition through to reliable production deployment, setting technical direction along the way. We are primarily hiring for senior and staff-level engineers who are comfortable owning critical systems end-to-end. This role involves owning systems that directly impact real patient outcomes. ## **What You’ll Do** - Own end-to-end ML systems, including architecture, data, modeling, evaluation, and production infrastructure - Train and fine-tune large language models (LLMs) for: - Clinical reasoning - Medical question answering - Evidence-grounded generation - Make and own tradeoffs across **accuracy, latency, cost, and safety** in high-stakes production environments - Develop evaluation frameworks to ensure model safety and clinical validity - Integrate ML systems into product workflows and patient-facing applications - Monitor system performance in production and iterate based on real-world usage and feedback - Define what “correct” means in ambiguous clinical workflows in collaboration with engineers and clinicians ## **What We’re Looking For** - Strong foundation in machine learning and software engineering - Track record of building and owning ML systems in production where performance, reliability, or correctness materially mattered - Experience driving ambiguous ML problems from 0→1, including problem formulation, model design, and 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 — you know when to use ML, when not to, and how to scope problems accordingly - Comfort working in a fast-moving, early-stage environment - Experience working on systems where decisions have real-world consequences (e.g., healthcare, finance, infrastructure) ## **Nice to Have** - Experience deploying LLMs in production environments - Experience building distributed systems or large-scale data pipelines - Experience working with clinical, biomedical, or other regulated datasets ## **Why Join Latent Health** - Work on high-stakes problems with real impact on patient care - Build systems that define how AI is trusted in clinical decision-making - Significant ownership in a small, high-caliber team - Competitive compensation and meaningful equity ## **Location** We are based in San Francisco and work together in person. We spend most of the week in the office and prioritize candidates who are excited to work this way. ## **Compensation** - Base salary: $225,000 – $300,000+ - Meaningful equity in an early-stage, Series A company ## **Closing** If you’re interested in building systems that bring truly personalized healthcare to millions of patients, we’d love to talk.