
Terra's AI platform models and optimizes complex projects across mineral exploration & natural resource projects. Terra enables faster designs, reduced capital costs, and improved energy/resource…

Terra's AI platform models and optimizes complex projects across mineral exploration & natural resource projects. Terra enables faster designs, reduced capital costs, and improved energy/resource…
Sector: AI for mineral and subsurface exploration
Product focus: Deposit characterization, uncertainty modeling, and drill planning optimization
Founders: John Mern, PhD; Anthony Corso, PhD
Employees: ~15
Notable funding: Seed (2023) and later convertible note (2025) reported
Mineral exploration and subsurface reservoir characterization; reducing exploration risk and capital cost for large-scale resource projects.
Mining / Natural resources AI
$3.4M
Seed round reported by company materials
Convertible note round reported in database entries; investors reported to include Founders Factory and Rio Tinto
“Investors reported to include Khosla Ventures, Storyhouse Ventures, Plug and Play, The TomKat Center for Sustainability, Climate Capital; Rio Tinto reported as strategic partner and investor”
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About Terra AI We are building the state-of-the-art AI platform for the discovery and development of clean energy and mineral resources. We bring the most advanced techniques in generative AI, foundation modeling, and autonomous decision optimization to tackle the most important problems in the geosciences. These systems can help more reliably identify critical resource deposits, more rapidly measure and characterize them, and design more efficient and sustainable production plans.
We are backed by Khosla Ventures and other leading venture investors. We are now looking to grow our team from ~15 to ~30 by the end of the year to continue to mature our technology and support deployment with our world-class mineral and clean energy partners.
Role Own customer data ingestion and build QA/QC workflows that make messy, real-world geoscience data usable at scale. This role blends hands-on GIS execution with data engineering. You will run GIS workflows for projects while also building automation to reduce manual work and improve repeatability.
What You’ll Do Run project GIS workflows
Build ingestion and QA/QC systems
Requirements
Nice to have
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Ingest and normalize datasets such as:
Drillhole and drill core data (major focus)
Airborne and other geophysical survey datasets
Supporting geospatial layers and project metadata
Standardize disparate client formats into a unified framework to support modeling workflows.
Build automated QA/QC checks that catch issues early, including:
Coordinate reference systems and transformations
Units, conventions, missingness, duplicates, and outlier detection
Schema validation, metadata sanity, provenance tracking
Cross-dataset consistency checks (for example, collars vs surveys vs intervals)
Create reproducible ingestion pipelines that reduce manual work and shorten time-to-first-model.
Act as a primary liaison for geology and ML teams, providing spatial analysis and visual validation of outputs against client-provided datasets.
Document standards and build tooling that is usable by other engineers and scientists.