Full Stack Engineer, Scientific Modeling Tools

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 cu [... source excerpt omitted ...] igor, candid communication, continual learning, and environmental stewardship to help modern exploration teams solve some of the world’s most important resource challenges. Role Productionize and extend internal modeling tools used to generate subsurface outputs. You will take software built around scientific workflows and make it robust, maintainable, and easier to run, inspect, and extend. This role is for someone who can bridge product-quality engineering with scientific computing. This team is building a durable foundation for multiple scientific domains, including geophysics and reservoir simulation. Candidates may lean toward one or the other, but the core engineering shap [... source excerpt omitted ...] ork primarily in Python and Julia. Integrate with ML-adjacent components and artifacts (inputs, outputs, model wrappers), without being responsible for inventing new ML methods. Requirements Strong software engineering fundamentals and proven ability to take ownership of complex codebases. Production-grade Python skills. Comfort working in Julia or willingness to go deep quickly. Experience designing APIs, handling configuration, and building reliable execution paths for complex workflows. Familiarity with performance profiling and optimization tooling. Familiarity with ML frameworks at an integration level (PyTorch preferred, TensorFlow or JAX also relevant), including artif