Research Software Engineer

Provides full-stack software support across our full range of data infrastructure projects. Ensures the code behind the multi-scale climate modeling project is reproducible, performant, and delightful for outside contributors. The purpose of this project is to prototype surrogate models, machine‑learned emulators trained on targeted high‑resolution simulations, designed to increase the accuracy and speed of modeling SAI deployment scenarios. Responsibilities include building sharable datasets, creating modular training and evaluation scripts, optimizing I/O, parallelism, and memory usage on large, distributed compute clusters. Packages models and dependencies as Docker / Conda environments and publishes to DockerHub and PyPI. Leads documentation, example notebooks, and dev-container setups. Fields community PRs and resolves bottlenecks/bugs.