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Company Overview
The employer is focused on scaling medical knowledge and developing reliable AI systems that can improve health outcomes. Its technology combines statistical learning with structured medical knowledge to create transparent and clinically grounded models. Health systems and insurers use these solutions to improve patient-level foresight, evidence deployment, and prediction.
Job Description
The role is suited for Machine Learning Engineers who thrive at the intersection of data science, modeling, and software engineering. The selected individual will design and implement models that learn from longitudinal healthcare data while balancing statistical rigor, interpretability, and scalability.
The position provides an opportunity to work on foundational machine learning challenges in healthcare, with work contributing to clinical, actuarial, and policy-related decision-making.
Responsibilities
Develop predictive models that forecast disease progression, healthcare utilization, and costs using temporal clinical data, including claims, EHR, laboratory, and pharmacy data.
Design interpretable and explainable machine learning solutions that can be evaluated and trusted by clinicians, actuaries, and other decision-makers.
Research and prototype novel approaches using classical and modern machine learning techniques.
Build robust and scalable ML pipelines for training, validation, and deployment within distributed computing environments.
Collaborate with data engineers, clinicians, domain experts, and product teams to align machine learning models with real-world healthcare needs.
Conduct exploratory data analysis in collaboration with domain experts.
Communicate findings and methodologies clearly through visualizations, technical documentation, and presentations.
Qualifications
Strong background in statistical modeling, machine learning, or data science.
Experience working with temporal or longitudinal data is preferred.
Strong proficiency in Python and relevant machine learning ecosystems such as PyTorch, JAX, NumPyro, or PyMC.
Demonstrated experience taking machine learning models from research prototypes through production deployment.
Strong software engineering skills and experience building production-quality ML systems.
Ability to clearly explain individual contributions, technical decisions, and measurable impact.
Familiarity with probabilistic methods, survival analysis, or Bayesian inference is preferred.
Healthcare industry experience is beneficial but not required.
Experience with clinical data or terminologies such as ICD, CPT, SNOMED CT, and LOINC is a plus.
Exposure to actuarial modeling, claims forecasting, or risk-adjustment methodologies is a plus.
What the Employer Offers
Competitive compensation and equity.
The opportunity to work alongside an interdisciplinary team of researchers, engineers, and clinicians.
Direct influence on the development of next-generation predictive infrastructure for healthcare.
Opportunities to publish, co-author patents, and contribute to the organization’s research direction.
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