About the Role
Exploration Geologist - Geological Sampling Design & Validation
About 4Point AI
4Point AI is developing AI systems for mineral exploration and subsurface understanding. We are looking for a geologist who can connect mineral-system interpretation with sampling design and meaningful testing of machine-learning predictions.
Role Overview
Our projects combine drillholes, assays, lithology and alteration logs, geophysics, maps, raster layers, remote sensing, and other spatial evidence. These sources differ in scale, resolution, dimensionality, density, and geological meaning. You will define geological populations and design training, validation, and testing across scales and data sources, extending beyond standard random or stratified splits.
What you will do
Define meaningful geological domains, states, transitions, structural settings, host-rock environments, alteration regimes, and mineralization styles relevant to the prediction problem.
Identify dominant, rare, transitional, redundant, and poorly represented conditions; assess whether training data cover the geology and data sources the model will encounter.
Design sampling across multiple scales and data sources, accounting for what drillhole, surface, geophysical, and raster observations reveal and their limitations.
Choose defensible sampling units: intervals, drillholes, cells, domains, structural environments, regions, or projects. Coordinate sampling at these nested levels.
Design independent tests using spatial separation, domain or drillhole holdouts, rare conditions, and cross-project evaluation where data allow. Reduce leakage from related observations and explain what each test establishes.
Distinguish interpolation within represented conditions from extrapolation into unseen geology; investigate failures caused by geological variability, sampling bias, scale mismatch, domain imbalance, or insufficient coverage.
Propose and test new sampling strategies, document a reusable framework, and recommend changes or further observations to resolve important gaps.
What you bring
Strong understanding of exploration geology and mineral systems, developed through a geology or related geoscience degree or equivalent experience.
Experience interpreting multiple geological and spatial datasets, including their meaning, resolution, scale, and limitations.
Strong reasoning about geological variability, population definition, sampling, uncertainty, and experimental design; the ability to turn that reasoning into a defensible sampling plan.
AI and technical experience
Statistics, geostatistics, spatial sampling, experimental design, mineral prospectivity mapping, or model validation experience is valuable. Understanding or willingness to learn ML sampling and validation is essential. Advanced ML implementation is optional; our ML team handles architecture, training, and metrics.
How we work
We value original ideas that can be tested and clearly documented. Experienced geologists, early-career practitioners, and master's or PhD candidates combining geology with computing or AI are welcome. A postgraduate degree is not required. Project and longer-term arrangements are open for discussion.
This role goes beyond model development. It focuses on integrating geoscience expertise with AI research to ensure that the models, data interpretations, assumptions, and conclusions are scientifically defensible and geologically meaningful.
You will help shape the scientific foundation of a Spatial AI company working toward increasingly general AI systems for mineral discovery.
You will have a high-impact role in a small technical team where strong ideas can directly influence the product, research direction, and company trajectory.
Benefits
Opportunity to work on challenging AI problems in mineral exploration
High-impact role in a small technical team
Research-driven environment
Opportunity to shape core scientific and technical workflows
Competitive compensation based on experience
This is a remote position.