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San Francisco, California, United States
Onsite
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.
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.
As a Machine Learning Engineer, Research, you will own the design and development of novel modeling approaches that advance state-of-the-art clinical intelligence.
You will drive research from ambiguous problem definition through to validated results and downstream impact, shaping the technical direction of how models learn from longitudinal patient data.
We are primarily hiring for senior and staff-level engineers who are comfortable owning critical research problems end-to-end.
This role involves working on problems that directly impact real patient outcomes.
Own research initiatives end-to-end, including problem formulation, experimental design, modeling, and evaluation
Develop novel architectures, training methods, and objectives leveraging longitudinal patient data
Work on verifiable reinforcement learning, mid-training, and post-training of foundation models
Design rigorous evaluation methodologies to assess model reasoning, correctness, and clinical relevance
Make and own tradeoffs between model capability, interpretability, and verifiability in high-stakes settings
Collaborate with clinicians and engineers to define meaningful problem formulations grounded in real-world workflows
Partner with ML engineers to ensure research translates into deployable systems
Strong foundation in machine learning, deep learning, or a related technical field
Track record of driving ML research or novel modeling work from idea to validated results
Experience working on ambiguous research problems with limited prior art
Hands-on experience with PyTorch or similar frameworks
Ability to operate independently in high-ambiguity environments with minimal guidance
Strong technical judgment — you can identify meaningful problems, design appropriate approaches, and evaluate results rigorously
Comfort working in a fast-moving, early-stage environment
Experience working on systems where decisions have real-world consequences (e.g., healthcare, finance, infrastructure)
Publications at top-tier ML venues (e.g., NeurIPS, ICML, ICLR)
Experience with LLMs, NLP, or sequence modeling
Experience with reinforcement learning or alignment methods
Experience working with longitudinal or structured data at scale
Experience working with clinical, biomedical, or scientific domains
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
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.
Base salary: $225,000 – $300,000+
Meaningful equity in an early-stage, Series A company
If you’re interested in building systems that bring truly personalized healthcare to millions of patients, we’d love to talk.
