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Position Summary:
We're hiring a Machine Learning Engineer to own the systems our models run on. You'll be working on the models themselves, the data and training pipelines, and the engineering around them.
Our research team builds probabilistic models that forecast how patients' health and costs change over time, trained on longitudinal healthcare data. They've shown the models work. Now we're hiring someone who has built production ML systems to help us improve how they run: reproducible training, trustworthy evaluation, clean packaging, versioned data and models, and scalable pipelines.
You'll work on our foundation forecasting models and the platform under them.
Background & Context:
The Organization's mission is to scale medical knowledge and create reliable AI for the health benefit of every person. We build neurosymbolic AI: systems that combine statistical learning with structured medical knowledge, so the models stay transparent and clinically grounded. Health plans, self-insured employers, health systems and life-sciences teams use our platform for patient-level forecasting of disease progression, utilization and cost.
Key Responsibilities:
Our stack today is Python and JAX/PyTorch for modeling. The models train on medical claims and EHR data and are evaluated with temporal backtests.
In your first year you'll:
Qualification & Experience:
You've shipped ML systems that other people depended on, and you kept them running after launch.