Senior Software Engineer, Scientific ML for Minerals

KoBold Metals; United States; Remote; USD 170,000 - 215,000; Full time

Job summary

KoBold Metals is seeking a seasoned software/ML engineer to apply machine learning to remote-sensing, drillhole, imaging, geophysics, and other mineral exploration data. Build scalable ML systems to support rapid, high-quality decision-making across exploration projects.

You will collaborate with data scientists and geologists, travel to exploration sites approximately twice yearly, and contribute to a cutting-edge scientific computing stack for mineral discovery involving copper, lithium,

Qualifications

• 5+ years of experience as a software engineer, data scientist or ML engineer.
• Track record of building production quality data processing solutions or tooling.
• Proficiency with ML concepts including statistical, traditional and deep-learning approaches.
• Proficiency in Python, including array-based packages such as xarray and numpy.
• Experience with measured scientific data and visualizing data for domain experts.
• Experience in MLops and building robust ML systems.

Responsibilities

• Architect, implement, and maintain foundational scientific computing libraries.
• Build tooling to accelerate ML progress and prototyping in Jupyter notebooks; develop frameworks for experimentation, evaluation, and simulation; organize models and outputs for repeatability.
• Collaborate with data scientists to build models predicting ore metal locations.

Skills

Python programming

Machine learning concepts

Data processing

MLops

Problem solving

Tools

xarray

numpy

Jupyter notebooks

Job description

KoBold Metals is seeking a seasoned software/ML engineer to apply machine learning to remote-sensing, drillhole, imaging, geophysics, and other mineral exploration data. Build scalable ML systems to support rapid, high-quality decision-making across exploration projects.

You will collaborate with data scientists and geologists, travel to exploration sites approximately twice yearly, and contribute to a cutting-edge scientific computing stack for mineral discovery involving copper, lithium,