Note: The job is a remote job and is open to candidates in USA. KoBold Metals builds AI models and novel sensors to guide mineral exploration and discovery of critical energy-transition metals. The Senior Software Engineer will apply software engineering and machine learning to scientific and mineral exploration data, building scalable computing libraries, experimentation tools, and production ML pipelines while collaborating with data scientists, geoscientists, and engineers.
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
Skills
- 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
- 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)
- 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
- Travel is approximately twice per year depending on project needs
- All candidates must be legally authorized to work in the United States or Canada
- At least 5 years of experience as a software engineer, data scientist or ML engineer, though most great candidates will have closer to 10
- Hands-on history with hardware, flight, space, lab instrumentation, or field-deployed systems
Benefits
- Remote work; candidates can be located anywhere in the United States or Canada.
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