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KoBold Metals is building AI-driven mineral exploration technology to accelerate discovery of critical metals (copper, lithium, nickel, cobalt) essential for the energy transition. The mining industry has become significantly less efficient at finding new deposits, requiring 10x more capital than 30 years ago. KoBold combines advanced AI models with novel sensor technology to guide exploration decisions, and has already proven its approach with a world-class copper discovery using materially less capital than industry norms.
As a Senior Software Engineer on the scientific computing team, you will architect and maintain foundational libraries for processing remote-sensing, drillhole, imaging, geophysics, and other mineral exploration data. You'll build scalable ML systems that enable high-speed, high-quality decisions for exploration projects, working alongside data scientists and geoscientists.
Key responsibilities include: architecting scientific computing libraries; integrating heterogeneous third-party and domain-specific tools into coherent pipelines; building tooling to accelerate ML velocity (Jupyter prototyping, experimentation frameworks, model pipelines); debugging across system boundaries (binaries, undocumented formats, deployed instrumentation); coaching team members on engineering best practices; and occasional travel (~2x/year) to exploration sites to observe impact and design new technologies.
You should have 5-10+ years as a software engineer, data scientist, or ML engineer with a track record of building production-quality data processing solutions. Required skills: Python proficiency (xarray, numpy), foundational ML knowledge (statistical, traditional, deep learning), experience with physical-world data (measurements, scientific instruments, imagery, geophysical data), data visualization for domain experts, and hands-on hardware/instrumentation experience. Domain knowledge in geoscience is a plus but not required; intellectual curiosity about mineral exploration is essential.
