• Architect, implement, and maintain foundational scientific computing libraries that will be used in KoBold’s mineral exploration analyses. • Build tooling to increase the velocity of our machine learning progress, including enabling rapid prototyping in Jupyter notebooks; build experimentation, evaluation, and simulation frameworks; turning successful R&D into robust, scalable ML pipelines; and organizing models and their outputs for repeatability and discoverability. • In collaboration with data scientists, build models to make statistically valid predictions about the locations of economic concentrations of ore metals within the Earth’s crust. • Apply–and coach team members to use–engineering best practices such as writing robust, testable and composable code • Collaborate with data scientists, geoscientists and engineers to invent the modern scientific computing stack for mineral exploration • Occasional travel to exploration sites around the world to observe the impact of scientific computing on KoBold’s exploration products and design new technologies to further discovery. Travel is approximately twice per year depending on project needs.