Machine Learning Engineer, Staff or Principal

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. 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 Rice and Canada Pension Plan Investments; technology venture capitalists Andreessen Horowitz, Breakthrough Energy Ventures, BOND Capital, and Standard Investments; and natural resources companies Equinor, BHP, and Mitsubishi.

About the position:

At KoBold we believe that a modern ML stack will enable systematic mineral exploration and materially improve the success rate. This role is a key ingredient to this strategy. As a member of our software engineering team, you will apply software engineering and machine learning to large remote-sensing datasets, geochemical assays, geophysical measurements, and many more. Your goals is 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 of this role include:

• Architect, implement, and maintain foundational scientific computing libraries that will be used in Kobold’s mineral exploration analyses.

• In collaboration with other engineers, build ML tooling to increase the velocity of our machine learning progress, including enabling rapid prototyping in Jupyter notebooks; building 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 compositional anomalies within the Earth’s crust.

• Apply–and coach team members to use–engineering best practices such as writing testable and composable code

• Collaborate with data scientists, geoscientists and engineers to invent the modern scientific computing stack for mineral exploration

Qualifications

Our ideal candidate will have:

• At least 10 years of experience as a software engineer, data scientist or ML engineer.

• Track record of building production ML solutions or tooling that have delivered business value

• Proficiency with foundational concepts of ML

• Proficiency in Python, ideally including array-based packages such as xarray and numpy

• Proficiency in a variety of parallel computing patterns, for example using distributed computing frameworks such as Dask

• Flexibility to engage with data scientists and increase their productivity for both experimental and production workflows 

• An open-mind and curious attitude to learn and embrace the unique challenges of applying machine learning to mineral exploration, such as limited groundtruth data, complex quality metric design, and difficulties to create generalizable models

• 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 communicator who enjoys working with colleagues across the company.

• Excitement about joining a fast-growing early-stage company, comfort with a dynamic work environment, and eagerness to take on a range of responsibilities.

• Keen not just to build cool technology, but to figure out what technical product to build to best achieve the business objectives of the company.

• Ability to independently prioritize multiple tasks effectively.

KoBold Metals is an equal opportunity workplace and an affirmative action employer. We are committed to equal employment opportunity for people of any race, color, ancestry, religion, sex, gender identity, sexual orientation, marital status, national origin, age, citizenship, marital status, disability, or veteran status.

The US base salary range for this full-time exempt position is $200,000-$275,000.

Location: Remote, Candidates can be located anywhere in the United States or Canada. All candidates must be legally authorized to work in the United States or Canada.