The posting
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 Khosla Ventures, BHP, and Rio Tinto, Terra AI works with industry leaders to bring a more informed, data-driven approach to critical resource development.
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.
