Senior Software Engineer, Scientific Computing

Remote

Responsibilities

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.
Collaborate with data scientists, geoscientists and engineers to invent the modern scientific computing stack for mineral exploration
Track record of building production quality data processing solutions or tooling that have delivered business value
Drive to increase the velocity and effectiveness of our data scientists in both experimental and production workflows

KoBold builds AI models for mineral exploration and deploys those models—alongside our novel sensors—to guide decisions on KoBold-owned-and-operated exploration programs.
As a member of our scientific computing team, you will apply software engineering and machine learning to remote-sensing, drillhole, imaging, geophysics and other mineral exploration data in order to build scalable ML systems to help make high-speed, high-quality decisions for our mineral exploration projects.
experience as a software engineer, data scientist or ML engineer, though most great candidates will have closer to 10.
Proficiency with foundational concepts of ML, including statistical, traditional and deep-learning approaches
Proficiency in Python, ideally including array-based packages such as xarray and numpy Deep
experience with measured scientific data •
Experience in visualizing scientific data for domain experts •
Experience in MLops and in the making of robust ML systems
Capacity to dive deep on novel challenging problems in applying ML to mineral exploration, including understanding a complex domain of geology and mineral exploration practices as well as working with limited, disparate and noisy data sources