About Xterra

Xterra is a Khosla Ventures-backed company building AI agents that reason about complex scientific problems. We’re not a wrapper around existing models, we’re training our own foundation models on top of large-scale proprietary datasets. This is a rare intersection of frontier AI and real-world scientific impact.

Xterra is still in stealth mode. Please reach out to us for a full picture.

About the Role

The team is looking for a geophysicist who is a practitioner first. Someone whose working history is hands-on data preparation, interpretation, and modeling. Someone who has been on deck doing the work, not reviewing it from a distance.

The ideal candidate operates comfortably across scales, from reading crustal architecture in a regional magnetic compilation to interrogating a ground IP survey to determine whether a chargeability response reflects sulphide or graphite. The ability to move between system-level thinking and fine-grained interpretation is fundamental to this role.

Core areas of responsibility:

• Geophysical method characterization. Systematize how each method detects or fails to detect different styles of mineralization. Build calibrated response profiles grounded in real deposits and petrophysical data. Define detection thresholds, sensitivity limits, and the conditions under which expected physical property relationships break down.

• Edge-case geophysics. The deposits that matter most are often the ones that do not behave like the textbook. This role requires someone who is rigorous about where standard assumptions fail.

• Legacy and imperfect data extraction. Most remaining exploration targets sit under cover, and most available geophysical data was not acquired with current questions in mind. The right candidate can evaluate what legacy surveys with wide line spacing and older instrumentation can still resolve, what they cannot, and where the boundary lies between a defensible interpretation and overreach.

• Cover and depth modeling. Characterize how laterite, transported regolith, calcrete, and basin fill attenuate or distort the geophysical response across methods, and incorporate those effects into detection assessments.

• Translation to AI and ML systems. Work with ML/AI engineers and data scientists to encode geophysical knowledge into features, constraints, and evaluation criteria. The right person does not need to be an ML expert, but they need to care about how their knowledge is represented, because if it is encoded incorrectly, the system reasons incorrectly.

Qualifications

Required

• MSc or PhD in geophysics or applied geophysics.

• 10+ years in mineral exploration geophysics with a hands-on, production-oriented work history. Data preparation, interpretation, modeling, ideally with experience supervising junior geophysicists.

• Deep working knowledge of ore mineral and host rock physical properties, and the instinct to question assumptions about them.

• Proficiency across the full method suite: magnetics, gravity, EM (airborne and ground), IP/resistivity, radiometrics, and MT.

• Experience across multiple deposit types, geological provinces, and ideally multiple continents.

• Experience interpreting in covered terranes where the geophysical response is complicated by regolith, transported cover, or basin fill.

• Strong communication & collaboration skills.

Preferred

• Familiarity with petrophysical databases, GIS platforms, and geophysical processing or inversion software.

• Exposure to ML/AI or data science concepts, enough to understand how geophysical knowledge feeds into a model.

• Python or scripting capability for data manipulation and visualization.