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Geoscience Machine Learning Jobs (NOW HIRING)

Terra AI

Senior/Staff Machine Learning Researcher

By combining advanced machine learning, probabilistic modeling, and deep geoscience expertise ... Terra AI helps exploration and mining companies make faster, more informed subsurface decisions ...

Terra AI

Senior/Staff Machine Learning Engineer

$107K - $146K/yr

By combining advanced machine learning, probabilistic modeling, and deep geoscience expertise ... Terra AI helps exploration and mining companies make faster, more informed subsurface decisions ...

Strayos

$13 - $17.50/hr

Over the course of this internship, you will work directly with our dynamic team of applied scientists who can share their deep knowledge of machine learning, remote sensing, geoscience, and physics.

Ascent Resources

Senior Geologist

Oklahoma City, OK · On-site

Knowledge of data analytics and machine learning applications in geology Education & Work Experience Required Minimum Requirements: * Bachelor's or master's degree in Geology or Geosciences * 10+ ...

Ascent Resources

Senior Geologist

Oklahoma City, OK · On-site

Knowledge of data analytics and machine learning applications in geology Education & Work Experience Required Minimum Requirements: * Bachelor's or master's degree in Geology or Geosciences * 10+ ...

Ascent Resources

Senior Geologist

Oklahoma City, OK · On-site

Knowledge of data analytics and machine learning applications in geology Education & Work Experience Required Minimum Requirements: * Bachelor's or master's degree in Geology or Geosciences * 10+ ...

Ascent Resources

Senior Geologist

Oklahoma City, OK

Knowledge of data analytics and machine learning applications in geology Education & Work Experience Required Minimum Requirements: * Bachelor's or master's degree in Geology or Geosciences * 10+ ...

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Geoscience Machine Learning information

See salary details

$25.5K

$42.6K

$88K

How much do geoscience machine learning jobs pay per year?

As of Sep 15, 2026, the average yearly pay for geoscience machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What is geoscience machine learning?

Geoscience machine learning is the application of machine learning techniques to analyze and interpret data related to Earth sciences, such as geology, geophysics, meteorology, and environmental science. Professionals in this field use algorithms and statistical models to identify patterns, make predictions, and extract meaningful insights from complex geoscientific datasets. This helps improve understanding of natural phenomena, supports resource exploration, and enhances environmental monitoring. Geoscience machine learning is increasingly important as data volumes grow and traditional analysis methods become less effective for large datasets.

What are the key skills and qualifications needed to thrive as a geoscience machine learning specialist?

To thrive as a Geoscience Machine Learning Specialist, you need a solid background in geosciences, data analysis, and machine learning, often supported by an advanced degree in geology, geophysics, or computer science. Proficiency with programming languages (such as Python or R), machine learning frameworks (like TensorFlow or Scikit-learn), and GIS or remote sensing tools is typically required. Strong problem-solving skills, collaboration, and effective communication are valuable soft skills for integrating data-driven insights with domain expertise. These competencies enable the development of accurate predictive models and innovative solutions to complex geoscientific challenges.

What are some common challenges faced by professionals working in geoscience machine learning roles?

Professionals in Geoscience Machine Learning often encounter challenges such as dealing with sparse, noisy, or incomplete data, especially when integrating geological and geophysical datasets. They must also navigate the complexity of domain-specific knowledge, requiring collaboration with geologists and geophysicists to ensure model validity. Additionally, scaling machine learning models to handle large volumes of spatial or temporal data can be technically demanding. Effective communication and interdisciplinary teamwork are crucial for translating model outputs into actionable insights for exploration, environmental management, or hazard assessment.

What is the difference between Geoscience Machine Learning vs Geoscientist?

AspectGeoscience Machine LearningGeoscientist
Required credentialsBackground in geoscience, programming, and data scienceDegree in geology, geophysics, or related field
Work environmentData analysis, modeling, and algorithm development in research or tech firmsFieldwork, laboratory, and research settings
Industry usageApplied in mineral exploration, seismic data interpretation, and environmental modelingConducts field surveys, sample analysis, and geological assessments

While both roles involve geoscience expertise, Geoscience Machine Learning focuses on applying data science and machine learning techniques to geoscientific problems, often in tech-driven environments. Geoscientists typically perform fieldwork and traditional research. The roles complement each other in modern geoscience projects.

What other helpful pages are available for Geoscience Machine Learning?

Other pages related to Geoscience Machine Learning:

Infographic showing various Geoscience Machine Learning job openings in the United States as of September 2026, with employment types broken down into 11% Internship, 56% Full Time, 11% Part Time, and 22% Contract. Highlights an 89% In-person, and 11% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Senior/Staff Machine Learning Researcher

Remote

Terra AI
See more jobs at Terra AIexternal

Full-time

Re-posted 3 days ago

Job description

Terra AI is building a new category at the intersection of artificial intelligence, geoscience, and critical resource development.
As global demand for copper, lithium, nickel, rare earth elements, geothermal energy, and other strategic resources accelerates, the mining and subsurface industries face a growing challenge: traditional exploration methods remain slow, expensive, and highly uncertain. Terra AI was founded to help solve this problem by redefining how critical resources are discovered, evaluated, and developed.
By combining advanced machine learning, probabilistic modeling, and deep geoscience expertise, Terra AI helps exploration and mining companies make faster, more informed subsurface decisions with greater confidence and capital efficiency. The company's platform integrates geological, geophysical, and drilling data into intelligent systems designed to improve targeting accuracy, accelerate discovery timelines, and reduce exploration risk.
Backed by leading investors including Khosla Ventures and working alongside strategic industry partners including Rio Tinto, Ero Copper, and Ramaco Resources, Terra AI is emerging as one of the more closely watched AI-native companies operating within the mining and critical minerals sector.
Terra AI's mission is to define the new global standard for data-driven critical resource development - breaking the cost and time curve required to support electrification, energy security, and the global energy transition.
The company operates with a strong partnership mentality, combining technical rigor, candid communication, continual learning, and environmental stewardship to help modern exploration teams solve some of the world's most important resource challenges.
Role description
In the same way image generators have shown the remarkable ability to produce a diverse set of realistic pictures conditioned on a text prompt (and other inputs), we are developing a generative model that produces 3D geological models conditioned on geophysical surveys, borehole measurements, and other forms of physical observation. The outputs of the generative model capture what we know and don't know about the state of the subsurface, allowing explorers to make maximally informed decisions about how and where to explore for critical resources.
We are looking for a talented deep learning engineer or scientist to lead the development of this model that will revolutionize decision-making in the earth subsurface for a wide range of clean energy applications.
Role Responsibilities
  • Design, train, test, and iterate on diffusion models for 3D geological models
  • Design, train, test, and iterate on an approach for conditioning generation on geophysical data and other observations
  • Inform the generation of synthetic data to improve model performance
  • Adapt diffusion modeling approach to specific real-world projects in collaboration with project teams.

Qualifications
Required Qualifications:
  • Extensive PyTorch Experience
    • Deep understanding of PyTorch, including writing custom modules, optimizing training, and debugging issues in large-scale models.
  • Expertise in Developing Large Deep Learning Models from Scratch
    • Proven ability to design, implement, and train complex deep learning architectures from the ground up.
  • Data Curation Skills
    • Hands-on experience in creating, cleaning, and maintaining high-quality datasets tailored for machine learning applications.
  • Strong Software Engineering and Design Experience
    • Proficient in software development best practices, including version control, testing, and code optimization.
    • Familiarity with designing scalable and maintainable systems.

Nice-to-haves:
  • Experience with Generative Models
    • Familiarity with generative architectures, particularly diffusion models, and an emphasis on posterior sampling methods.
  • Knowledge of Transformer Architectures
    • Experience building and training transformers, especially in applications involving 3D data.
  • Scaling Models Across Large GPU Clusters
    • Expertise in parallelizing models across multiple GPUs and optimizing distributed training pipelines.
  • Cloud Infrastructure Expertise
    • Experience setting up, managing, and optimizing cloud environments for machine learning workloads, including provisioning resources and managing costs.

Apply