Research Scientist

Latent; San Francisco (CA); On-site; USD 225,000 - 300,000; Full time

Benefits offered by this job

Competitive compensation

Meaningful equity

Ownership in a small team

Job summary

A healthcare technology firm in San Francisco is looking for an experienced Research Scientist specializing in Machine Learning to advance clinical intelligence models. You will own research projects from start to finish, impacting real patient outcomes by developing novel modeling approaches. Candidates should have a strong foundation in machine learning, experience with PyTorch, and the ability to work successfully in ambiguous environments. Competitive compensation, including equity, is offered in a fast-paced, high-impact role.

Qualifications

• Strong foundation in machine learning and deep learning.
• Experience driving ML research or modeling work from concept to validation.
• Hands-on experience with PyTorch or similar frameworks.
• Ability to work independently in a fast-moving environment.

Responsibilities

• Own research initiatives from problem formulation to evaluation.
• Develop novel architectures and training methods using patient data.
• Design rigorous methodologies for model evaluation.
• Collaborate with clinicians to define problem formulations.

Skills

Machine learning

Deep learning

PyTorch

Independent problem-solving

Technical judgment

High-stakes decision-making

Job description

Research Scientist

About Latent Health

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.

ML at Latent Health

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.

The Role

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.

What You’ll Do

• 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
  

What We’re Looking For

• 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)
  

Nice to Have

• 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
  

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
  

Location

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.

Compensation

• Base salary: $225,000 – $300,000+
  

• Meaningful equity in an early-stage, Series A company
  

Closing

If you’re interested in building systems that bring truly personalized healthcare to millions of patients, we’d love to talk.