Machine Learning Engineer

Machine Learning EngineerAbout Latent HealthHealthcare 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 HealthThe Machine Learning team is responsible for building systems that run in real clinical workflows.We work on:Verifiable reinforcement learning at scaleMid-training and post-training of foundation modelsNovel objectives derived from longitudinal patient dataWe 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 RoleAs 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 DoOwn end-to-end ML systems, including architecture, data, modeling, evaluation, and production infrastructureTrain and fine-tune large language models (LLMs) for:Clinical reasoningMedical question answeringEvidence-grounded generationMake and own tradeoffs across accuracy, latency, cost, and safety in high-stakes production environmentsDevelop evaluation frameworks to ensure model safety and clinical validityIntegrate ML systems into product workflows and patient-facing applicationsMonitor system performance in production and iterate based on real-world usage and feedbackDefine what “correct” means in ambiguous clinical workflows in collaboration with engineers and cliniciansWhat We’re Looking ForStrong foundation in machine learning and software engineeringTrack record of building and owning ML systems in production where performance, reliability, or correctness materially matteredExperience driving ambiguous ML problems from 0→1, including problem formulation, model design, and productionizationHands-on experience with PyTorch or similar frameworksAbility to operate independently in high-ambiguity environments with minimal guidanceStrong product and engineering judgment — you know when to use ML, when not to, and how to scope problems accordinglyComfort working in a fast-moving, early-stage environmentExperience working on systems where decisions have real-world consequences (e.g., healthcare, finance, infrastructure)Nice to HaveExperience deploying LLMs in production environmentsExperience building distributed systems or large-scale data pipelinesExperience working with clinical, biomedical, or other regulated datasetsWhy Join Latent HealthWork on high-stakes problems with real impact on patient careBuild systems that define how AI is trusted in clinical decision-makingSignificant ownership in a small, high-caliber teamCompetitive compensation and meaningful equityLocationWe 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.CompensationBase salary: $225,000 – $300,000+Meaningful equity in an early-stage, Series A companyClosingIf you’re interested in building systems that bring truly personalized healthcare to millions of patients, we’d love to talk.