2+ years of applied machine learning research experience with demonstrated work in at least one of: causal inference and structural causal models, Bayesian networks and probabilistic graphical models, probabilistic programming (Pyro, NumPyro, Stan, PyMC, or similar), or learned optimization and planning. The role focuses on the research-to-code path for model generation, planning, and optimization, including formulating models, building the training and inference pipelines, integrating with synthetic and laboratory datasets, and delivering trained, containerized frameworks.