Senior Software Engineer, Decision Science

Senior Software Engineer, Decision Science 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. Vantage Compute builds AI models for mineral exploration and deploys those models—alongside our novel sensors—to guide decisions on Vantage Compute-owned-and-operated exploration programs. In the six years since founding, Vantage Compute 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. Vantage Compute 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. Vantage Compute has a portfolio of more than 60 other projects, each of which has the potential for another high-quality discovery. Vantage Compute 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 Vantage Compute 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 decision science techniques in order to build scalable systems to help make high-speed, high-quality decisions for our mineral exploration projects. Collaborating with our exceptional team of data scientists, software engineers, 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 decision science libraries that will be used in Vantage Compute’s mineral exploration analyses. Build tooling to increase the velocity of our decision making, including enabling rapid prototyping in Jupyter notebooks; build experimentation, evaluation, and simulation frameworks; turning successful R&D into robust, scalable pipelines; and organizing ML models and their outputs for repeatability and discoverability. 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 decision science technology for mineral exploration Occasional travel to exploration sites around the world to observe the impact of scientific computing on Vantage Compute’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 in the field of decision science with a strong software engineering focus, 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 decision science 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 workin