Latent Engineering

Machine Learning Engineer

San Francisco

Sponsorship not specified$225k-$300kDetected 100 days ago

Distributed SystemsMachine LearningPyTorchData EngineeringLLMsPatient CareCollaboration

About the role

We are primarily hiring for senior and staff-level engineers who are comfortable owning critical systems end-to-end.
This role involves owning systems that directly impact real patient outcomes.

Own end-to-end ML systems, including architecture, data, modeling, evaluation, and production infrastructure
Make and own tradeoffs across accuracy, latency, cost, and safety in high-stakes production environments
Develop evaluation frameworks to ensure model safety and clinical validity
Track record of building and owning ML systems in production where performance, reliability, or correctness materially mattered
Experience driving ambiguous ML problems from 0→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 - you know when to use ML, when not to, and how to scope problems accordingly
Comfort working in a fast-moving, early-stage environment

Experience deploying LLMs in production environments
Experience working with clinical, biomedical, or other regulated datasets
Work on high-stakes problems with real impact on patient care
Significant ownership in a small, high-caliber team
We spend most of the week in the office and prioritize candidates who are excited to work this way.

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.
Clinical reasoning
Medical question answering
Evidence-grounded generation
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

Machine Learning Engineer
About Latent Health
Strong foundation in machine learning and software engineering
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.

This listing is sourced directly from Latent Engineering's careers page and normalized into a canonical job model.