Latent Health is hiring a Machine Learning Engineer to build end-to-end, production-grade ML systems that operate in real clinical workflows. This role trains and deploys ML and LLM systems for clinical reasoning and medical question answering, with evaluation, safety, and iteration built into day-to-day production work. The team is small, focused, and designed for engineers to take ownership of critical systems.
What you’ll work on
- Own end-to-end ML systems, from architecture and data to modeling, evaluation, and production infrastructure
- Train and fine-tune large language models (LLMs) for clinical reasoning
- Train and fine-tune LLMs for medical question answering
- Train and fine-tune LLMs for evidence-grounded generation
- Make and own tradeoffs across accuracy, latency, cost, and safety in high-stakes production environments
- Develop evaluation frameworks to support model safety and clinical validity
- Integrate ML systems into product workflows and patient-facing applications
- Monitor 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
- Drive systems from ambiguous problem definition through reliable production deployment
- Own systems that directly impact real patient outcomes
Skills and experience
- Strong foundation in machine learning and software engineering
- Track record building and owning ML systems in production where performance, reliability, or correctness materially mattered
- Experience driving ambiguous ML problems from 0 to 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, including knowing when to use ML and how to scope problems
- 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
Technologies
- PyTorch
- Large language models (LLMs)
Compensation and equity
- Base salary: $225,000 – $300,000+
- Meaningful equity in an early-stage, Series A company
- Competitive compensation and meaningful equity
Team and work style
Latent Health is based in San Francisco and works together in person. The team spends most of the week in the office and prioritizes candidates who are excited to work this way.
ML at Latent Health
The Machine Learning team builds systems that run in real clinical workflows. Current areas include verifiable reinforcement learning at scale, mid-training and post-training of foundation models, and novel objectives derived from longitudinal patient data. This is a small group of researchers and engineers focused on pushing the frontier while shipping real systems into production. Engineers are expected to take ownership of critical systems, not just components.
About Latent Health
Care is often fragmented, and medical history is scattered across systems that do not communicate. Physicians have limited time to interpret decades of context, and when something goes wrong, patients are left with tools that understand medicine broadly but not the individual. Latent Health is building systems designed to understand both the population (clinical knowledge at scale) and the individual (longitudinal patient history), answering complex clinical questions with patient-specific context and verifiable reasoning.
