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 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. WHO YOU ARE You’re an AI/ML researcher who enjoys tackling problems without an established playbook. You’re excited by the challenge of developing novel generative models, testing new approaches, and using rigorous experimentation to understand what works, what fails, and why. You combine strong PyTorch skills with the ability to design and train substantial models from scratch. You know that meaningful research depends on thoughtful data curation, reproducible experiments, and software that others can understand and build upon. You enjoy collaborating across machine learning, geoscience, and engineering. You’re excited by the opportunity to apply generative AI to a genuinely difficult physical-world problem: understanding the geology we cannot directly see. Prior geoscience experience is welcome, but curiosity and a willingness to learn matter just as much. What you’ll do Terra AI is developing generative models that produce three-dimensional geological representations conditioned on geophysical surveys, borehole measurements, and other physical observations. Like image-generation systems that produce different plausible images from a prompt, our models aim to produce plausible views of the subsurface consistent with the evidence available. The goal is not to pretend we know exactly what lies underground. It is to capture both what we know and what remains uncertain, giving exploration teams a stronger foundation for deciding where and how to investigate critical resources. In this role, you’ll help lead the research and development of these models, from exploring architectures and conditioning methods to training, evaluation, and adaptation to real projects. You’ll work alongside AI researchers, engineers, geoscientists, and project teams to connect scientific advances with consequential real-world decisions. Core responsibilities - Design, implement, train, evaluate, and iterate on diffusion models that generate 3D geological models. - Develop approaches to condition model outputs on geophysical surveys, borehole measurements, and other physical observations. - Shape synthetic-data generation and data-curation approaches to improve model quality, robustness, and generalization. - Form hypotheses, design rigorous experiments, and evaluate model behavior, uncertainty, and limitations using reproducible training workflows. - Adapt generative modeling approaches to real-world projects in close collaboration with geoscience and delivery teams. - Develop clear, maintainable research code and collaborate with engineering teams to make promising approaches reproducible and useful beyond a single experiment. Qualifications Required - Extensive hands-on PyTorch experience, including custom modules, training optimization, and debugging complex or large-scale models. - Demonstrated experience designing, implementing, and training substantial deep learning architectures from scratch. - Strong data-curation skills, including creating, cleaning, validating, and maintaining datasets for machine learning. - Solid software engineering and system-design fundamentals, including version control, testing, code optimization, scalability, and maintainability. Nice to have - Experience with generative architectures, especially diffusion models and posterior sampling methods. - Experience developing and training transformer architectures, particularly for 3D or other structured data. - Experience scaling model training across multi-GPU systems or large GPU clusters, including distributed training optimization. - Experience managing and optimizing cloud infrastructure for machine learning workloads. We recognize that exceptional candidates come from a range of research, engineering, and scientific backgrounds. If this work excites you and your experience aligns with the spirit of the role, we encourage you to apply even if you do not meet every preferred qualification. Compensation & Benefits The annual base salary range for this position is $175,000–$265,000. Actual compensation will depend on experience, qualifications, skills, location, and other job-related factors. This role is eligible for equity and Terra AI’s benefits package, including medical, dental, vision, life insurance, FSA, and a 401(k) with company match. Any additional incentive compensation will be communicated separately. Applications may be submitted through the job posting; the application closing date will be provided when the role is published. Terra AI is an equal opportunity employer. We make employment decisions based on qualifications, merit, and business needs, without regard to race, color, ancestry, national origin, religion, creed, age, disability, medical condition, genetic information, sex, gender, pregnancy or related medical conditions, sexual orientation, gender identity or expression, marital status, reproductive health decision-making, military or veteran status, or any other characteristic protected by applicable federal, state, or local law. We’re committed to building a workplace where people with different backgrounds, experiences, and perspectives can do their best work. Terra AI also provides reasonable accommodations to qualified applicants and employees with disabilities. If you need an accommodation during the application or interview process, please let us know.

Requirements

Must-haves

Terra AI is building a new category at the intersection of artificial intelligence, geoscience, and critical resource development.
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.
What you’ll do
Design, implement, train, evaluate, and iterate on diffusion models that generate 3D geological models.
Develop approaches to condition model outputs on geophysical surveys, borehole measurements, and other physical observations.
Shape synthetic-data generation and data-curation approaches to improve model quality, robustness, and generalization.
Form hypotheses, design rigorous experiments, and evaluate model behavior, uncertainty, and limitations using reproducible training workflows.
Adapt generative modeling approaches to real-world projects in close collaboration with geoscience and delivery teams.
Develop clear, maintainable research code and collaborate with engineering teams to make promising approaches reproducible and useful beyond a single experiment.
Extensive hands-on PyTorch experience, including custom modules, training optimization, and debugging complex or large-scale models.
Demonstrated experience designing, implementing, and training substantial deep learning architectures from scratch.
Strong data-curation skills, including creating, cleaning, validating, and maintaining datasets for machine learning.
Solid software engineering and system-design fundamentals, including version control, testing, code optimization, scalability, and maintainability.

Nice-to-haves

Experience with generative architectures, especially diffusion models and posterior sampling methods.
Experience developing and training transformer architectures, particularly for 3D or other structured data.
Experience scaling model training across multi-GPU systems or large GPU clusters, including distributed training optimization.
Experience managing and optimizing cloud infrastructure for machine learning workloads.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningml

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)