Engineering Manager, Data Systems

Overview

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. In the six years since founding, 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, Step Stone, and Standard Investments; and natural resources companies Equinor, BHP, and Mitsubishi.

About the role: At KoBold, we believe making mineral exploration data broadly accessible to humans and machines will enable systematic exploration and materially improved exploration success rates. This role is a key ingredient to this strategy. As an early Engineering Manager on the Data Systems Engineering team, you will have the opportunity to shape how we build our product and organization to realize this vision.

Through development of our robust data systems, we will empower KoBold to unlock invaluable insights and streamline intricate scientific processes. Collaborating with our exceptional team of data scientists and geologists, you will have the opportunity to tackle complex scientific problems head-on and collectively pave the way for the 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 of this role include:

• Thoughtfully build and lead a team of high-performing software engineers, hiring and growing diverse individuals on your team
• Leverage your team in collaboration with data scientists, geoscientists, and other software engineers to plan, prioritize, and deliver impactful projects that accelerate our exploration efforts
• Work with the Head of Data Systems Engineering and interdisciplinary leaders across the company on our strategy to advance both our technical product and organization
• Collaborate with cross functional peers (geoscientists and data scientists) across the company, learning about mineral exploration. Coach your team to better partner across the company.
• Spend roughly 1/3rd of your time contributing to projects as an individual contributor

Our ideal candidate will have:

• 8+ years experience building production software systems
• 3+ years in an engineering leadership role with reports
• Experience building robust distributed cloud data systems. Bonus for experience building systems managing large geospatial or geoscientific datasets.
• Deep understanding of multiple database and data storage technologies
• A track record of taking ownership from definition of the problem and delivering large projects with demonstrated impact in an iterative manner
• Skilled at dealing with ambiguity and prioritizing effectively from multiple inputs
• Formal education or job exposure to natural sciences