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KoBold Metals

AI-driven mineral exploration for critical metals

Senior Software Engineer - Scientific Computing

Applied Machine LearningConfirmed live in the last 24 hours
Full-time$170k - $215k

About the Job

Requirements

  • At least 5 years of experience as a software engineer, data scientist, or machine learning engineer; most strong candidates will have closer to 10 years.
  • A track record of building production-quality data-processing solutions or tooling that have delivered business value.
  • Proficiency with foundational machine-learning concepts, 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 visualizing scientific data for domain experts.
  • Experience in machine-learning operations and building robust machine-learning systems.
  • Ability to apply machine learning to mineral exploration, understand geology and mineral-exploration practices, and work with limited, disparate, and noisy data sources.
  • Ability to take ownership and responsibility for large projects.
  • Intellectual curiosity and eagerness to learn about mineral exploration, particularly geology, and willingness to work directly with geologists in the field.
  • Ability to explain technical problems to and collaborate on solutions with domain experts who are not software developers.
  • Comfort with a dynamic work environment and ability to take on a range of responsibilities.
  • Ability to identify technical products that best achieve business objectives.
  • Ability to independently prioritize multiple tasks effectively.

Responsibilities

  • Architect, implement, and maintain foundational scientific-computing libraries for mineral-exploration analyses.
  • Build tooling to accelerate machine-learning progress, including enabling rapid prototyping in Jupyter notebooks; building experimentation, evaluation, and simulation frameworks; turning successful research and development into robust, scalable machine-learning pipelines; and organizing models and their outputs for repeatability and discoverability.
  • Collaborate with data scientists to build models that make statistically valid predictions about locations of economic concentrations of ore metals within the Earth's crust.
  • Apply engineering best practices, including writing robust, testable, and composable code, and coach team members to use them.
  • Collaborate with data scientists, geoscientists, and engineers to develop a modern scientific-computing stack for mineral exploration.
  • Travel to exploration sites around the world approximately twice per year, depending on project needs, to observe the impact of scientific computing on exploration products and design technologies to further discovery. Employees are expected to travel to project sites at least one week per year.

About the Company

KoBold Metals

AI-driven mineral exploration for critical metals

KoBold Metals uses artificial intelligence to accelerate the discovery of lithium, cobalt, copper, and nickel deposits for clean energy. Its main product is an AI-driven mineral exploration program that combines a proprietary EOI tool with large-scale data about the Earth’s crust to decide which data to collect at each exploration step and reduce uncertainty. It stands out from traditional explorers through heavy data gathering, a multidisciplinary team, and a data-powered, repeatable scientific process. Its goal is to locate and sell mineral deposits to clean-energy companies, speeding the supply of critical minerals for batteries and renewable energy technologies.

Company size
201-500
Company stage
Late Stage VC
Total funding
$1.1B
Headquarters
Berkeley, CA
Founded
2018

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