Latent Health is seeking a Machine Learning Engineer to lead end-to-end, production-grade ML systems used in real clinical workflows. The role centers on training and deploying ML and LLM solutions for clinical reasoning and medical question answering, with an emphasis on evaluation, safety, and iterative improvement after release.
Key Responsibilities
- Own end-to-end machine learning systems, covering architecture, data, 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 that support model safety and clinical validity
- Integrate ML systems into product workflows and patient-facing applications
- Monitor production performance 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 dependable production deployment, setting technical direction as needed
- Own systems that directly influence real patient outcomes
Required Qualifications
- Strong foundation in machine learning and software engineering
- Proven experience building and owning ML systems in production where performance, reliability, or correctness were materially important
- Experience taking 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 apply ML, when not to, and how to scope effectively
- Comfort working in a fast-moving, early-stage environment
- Experience on systems where decisions have real-world consequences (for example healthcare, finance, or infrastructure)
Technologies
- PyTorch
- Large language models (LLMs)
Compensation and Benefits
- Competitive compensation with meaningful equity
- Base salary: $225,000 to $300,000+ per year
- Meaningful equity in an early-stage, Series A company
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
Location and Work Model
The role is based in San Francisco, CA and is onsite. Latent Health works together in person, spending most of the week in the office, and prioritizes candidates who are comfortable with this setup.
Machine Learning at Latent Health
The Machine Learning team builds systems that run in real clinical workflows. Current work includes verifiable reinforcement learning at scale, mid-training and post-training of foundation models, and novel objectives derived from longitudinal patient data. The team is small and expects engineers to take ownership of critical systems, not just components.
About Latent Health
Latent Health is focused on rebuilding healthcare personalization beyond fragmented records and limited clinician time. The company aims to deliver models that account for both population-level clinical knowledge and longitudinal patient history. Its dataset reflects one of the most clinically diverse populations in the United States, including patients with chronic illness and complex disease, with deep patient records.
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
