San Francisco, CA (Onsite) Direct Hire Company Mission Our client's mission is to scale medical knowledge and create reliable AI for the health benefit of every person. The company is developing a new class of neurosymbolic AI systems, combining statistical learning with structured medical knowledge to create transparent, clinically grounded models. Its early adopters include health systems and insurers using the technology to improve patient-level foresight, evidence deployment, and prediction. The company's culture is collaborative, curious, and first-principles-driven. The team values individuals who are not only excellent implementers but also original thinkers—people who can reason clearly about complex systems and translate that understanding into production-quality solutions. The Role The client is seeking 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 rigor, interpretability, and scalability. This is an opportunity to work on foundational modeling challenges in healthcare, where the individual's work will directly inform clinical, actuarial, and policy decisions. Responsibilities Develop predictive models that forecast disease progression, utilization, and cost using temporal clinical data, including claims, EHR, laboratory, and pharmacy data. Design interpretable and explainable ML solutions that can be trusted by clinicians, actuaries, and decision-makers. Research and prototype novel approaches using both classical and modern machine learning techniques. Build robust, scalable ML pipelines for training, validation, and deployment within distributed computing environments. Collaborate with data engineers, clinicians, domain experts, and product teams to align models with real-world healthcare needs. Conduct exploratory data analysis in partnership with domain experts. Clearly communicate findings and methodologies through visualizations, technical documentation, and presentations. Requirements 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 ML ecosystems, such as PyTorch, JAX, NumPyro, or PyMC. Demonstrated experience taking 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 Client 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 company's research direction.
