Machine Learning Engineer

Latent Health is seeking a Machine Learning Engineer to build and own production-grade ML systems used in real clinical workflows. The role covers end-to-end development, from model training and evaluation to deployment, monitoring, and iteration for clinical reasoning and medical question answering.

Key Responsibilities

• Own end-to-end machine learning systems, including architecture, data, modeling, evaluation, and production infrastructure
• Train and fine-tune large language models (LLMs) for clinical reasoning
• Train and fine-tune large language models (LLMs) for medical question answering
• Train and fine-tune large language models (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 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
• Drive projects from ambiguous problem definition through reliable production deployment, setting technical direction along the way
• Own systems that directly impact real patient outcomes

Required Qualifications

• 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 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 for scoping problems and deciding when to use ML
• Comfort working in a fast-moving, early-stage environment
• Experience working on systems where decisions have real-world consequences (examples include healthcare, finance, and infrastructure)

Technologies

• PyTorch
• Large language models (LLMs)

Location and Work Style

The role is based in San Francisco, CA and is onsite. The team works together in person, spending most of the week in the office and prioritizing candidates who are interested in this work arrangement.

Compensation and Benefits

• Competitive compensation and meaningful equity
• Base salary: $225,000 – $300,000+ (USD 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

Machine Learning at Latent Health

• The Machine Learning team builds systems that run in real clinical workflows
• Work includes verifiable reinforcement learning at scale
• Work includes mid-training and post-training of foundation models
• Work includes novel objectives derived from longitudinal patient data
• The team is small and expects engineers to take ownership of critical systems, not only components
• The group includes researchers and engineers focused on shipping real systems into production

About Latent Health

Latent Health is focused on rebuilding healthcare with both population-level clinical knowledge and longitudinal patient history. The company’s models are designed to answer complex clinical questions with patient-specific context and verifiable reasoning.

The dataset represents one of the most clinically diverse populations in the United States, including patients with chronic illness and complex disease. Each patient record includes substantial depth.

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