KoBold Metals · 6 hours ago

Machine Learning Engineer, Remote Sensing - Staff or Principal

United States

Full-time

Remote

Senior Level, Lead/Staff

$200K/yr - $300K/yr

5+ years exp

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Artificial Intelligence (AI)Mineral

No H1B

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Responsibilities

Architect, implement, and maintain foundational scientific computing libraries for distributed processing of large-scale geospatial rasters, to be used in Kobold’s mineral exploration analyses.

In collaboration with other engineers, build tooling to increase the velocity of our machine learning progress on geospatial raster data, 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.

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 robust, testable and composable code

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

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Machine LearningPythonData ProcessingDistributed ComputingGeospatial AnalysisStatistical ApproachesDeep LearningMultispectral Remote-SensingSparkDask

Required

At least 5 years of experience as a software engineer, data scientist or ML engineer, 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

Proficiency in scaling complex data operations across distributed computing resources, using tools such as Spark or Dask

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 ML 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)

Experience with multispectral remote-sensing data from a variety of sources

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.

Ability to explain technical problems to and collaborate on solutions with domain experts who aren’t software developers.

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.