ML Engineer

Overview In this ML Engineer role, you will design and implement models that learn from longitudinal healthcare data while balancing rigor, interpretability, and scalability. You will work at the intersection of data science, modeling, and software engineering to produce production-ready solutions. Your work informs clinical and policy decisions, with a focus on transparent, clinically grounded models. You will collaborate with clinicians, data engineers, and product teams to shape next-generation predictive infrastructure in healthcare. Compensation / Benefitscompetitive compensationequityinterdisciplinary teamworkopportunities to publishco-author patentsinfluence on predictive infrastructure ResponsibilitiesDevelop predictive models for disease progression, utilization, and cost using longitudinal clinical dataCreate interpretable ML solutions trusted by clinicians and decision-makersResearch and prototype both classical and modern ML approachesBuild robust, scalable ML pipelines for training, validation, and deploymentCollaborate with data engineers, clinicians, domain experts, and product teamsConduct exploratory data analysis with domain partnersCommunicate findings through visualizations, documentation, and presentationsTranslate research prototypes into production-ready systems Key requirementsStrong background in statistical modeling, ML, or data scienceExperience with temporal/longitudinal data preferredProficiency in Python and ML ecosystems (PyTorch, JAX, NumPyro, PyMC)Proven track record of moving models from research to productionStrong software engineering skills for production-quality MLAbility to explain contributions, decisions, and impactFamiliarity with probabilistic methods, survival analysis, or Bayesian inference preferredHealthcare data experience beneficial but not requiredExperience with clinical terminologies (ICD, CPT, SNOMED CT, LOINC) is a plusExposure to actuarial modeling or risk-adjustment methods is a plusStrong communicationCollaborative mindsetAnalytical thinkerPythonPyTorchJAX