Senior Software Engineer, Scientific Computing
About the Company
The mining industry has steadily become worse at finding new ore deposits, requiring >10X more capital to make discoveries compared to 30 years ago. The easy-to-find, near-surface deposits have largely been found, and the industry has chronically under-invested in new exploration technology, relying on the manual techniques of yesteryear – even as demand accelerates for copper, lithium, and other metals to build electric vehicles, renewable energy, and data centers.
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. Since our founding in 2018, KoBold has become by far both the largest independent mineral exploration company and the largest exploration technology developer. Our data scientists and software engineers, who come from leading technology companies, jointly lead exploration programs with our renowned exploration geologists.
KoBold has proven its first discovery with materially less capital than the industry average and found one of the best copper deposits ever discovered: the copper is far more concentrated than the global average of copper mines, and this asset alone is expected to generate meaningful revenue for decades. KoBold has a portfolio of more than 60 other projects, each of which has the potential for another high-quality discovery.
KoBold is privately held; investors include institutional asset managers T. Rowe Price and Canada Pension Plan Investments; technology venture capitalists Andreessen Horowitz, Breakthrough Energy Ventures, BOND Capital, Durable Capital, StepStone, and Standard Investments; and natural resources companies Equinor, BHP, and Mitsubishi.
About the Role
At KoBold we believe that a modern scientific computing stack will enable systematic mineral exploration and materially improve our rate of mineral discovery. This role is a key ingredient to this strategy. 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. Collaborating with our exceptional team of data scientists and geologists, you will tackle complex scientific problems head-on and collectively pave the way for discoveries of vital energy transition metals like lithium, copper, nickel, and cobalt. Together we can shape the future of mineral exploration and contribute to building a sustainable world.
Responsibilities
- Architect, implement, and maintain foundational scientific computing libraries that will be used in KoBold’s mineral exploration analyses.
- Integrate third-party and domain-specific tooling — code you did not build and cannot modify — into a coherent, repeatable pipeline: getting a heterogeneous stack to exchange data and behave correctly.
- 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.
- Debug across boundaries — third-party binaries, undocumented formats, other people's code, and deployed instrumentation — rather than escalating at the edge of your own module.
- 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.
Qualifications
Our ideal candidate will have
- At least 5 years of experience as a software engineer, data scientist or ML engineer, though most great candidates will have closer to 10.
- Work experience across many distinct technical problem types.
- 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.
- Experience with data that comes from the physical world: physical measurements, scientific instrument output, imagery, or geophysical data.
- Experience in visualizing scientific data for domain experts.
- Hands-on history with hardware, flight, space, lab instrumentation, or field-deployed 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 new technologies 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).
Work practices and motivation:
- Ability to take ownership and responsibility of large projects.
- Intellectual curiosity and eagerness to learn about all aspects of mineral exploration, particularly in the geology domain. Open to working directly with geologists in the field. Enjoys constantly learning such that you are driving insights and innovations.
- Ability to explain technical problems to and collaborate on solutions with domain experts who aren’t software developers. A strong communica
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