KoBold Metals logo
KoBold Metals

KoBold Metals discovers the battery minerals containing Ni, Cu, Co, and Li critical for the electric vehicle revolution.

Senior Software Engineer, Scientific Computing

Full-stack EngineerSoftware EngineerFull TimeRemoteSeniorTeam 51-200Since 2019H1B No SponsorCompany SiteLinkedIn

Location

United States

Posted

1 day ago

Salary

$170K - $215K / year

Seniority

Senior

Bachelor Degree5 yrs expEnglishNumpyPython

Job Description

Senior Software Engineer, Scientific Computing

KoBold Metals

• 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.

Job Requirements

  • At least 5 years of experience as a software engineer, data scientist or ML engineer, though most great candidates will have closer to 10.
  • Track record of building production quality data processing solutions or tooling that have delivered business value
  • 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
  • Drive to increase the velocity and effectiveness of our data scientists in both experimental and production workflows
  • 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
  • Collaborative attitude to work with stakeholders with different backgrounds (data scientists, geoscientists, software engineers, operations)

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