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Founding Applied Scientist

Terranox AI

San Francisco, CAFull-timeSeen 1mo agoStill listed 2 days ago

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At a glance

Compensation
No compensation found
Location
San Francisco, CA
Schedule
Full-time
Work Authorization
Not specified

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Requirements

Credentials this posting asks for.

Doctorate

Job overview

Terranox AI is the first AI‑powered uranium discovery company applying AI/ML to geoscientific data to improve targeting decisions in clean energy. The founding applied scientist will anchor scientific foundations, lead data acquisition, processing, integration, and model development across geophysical, geochemical, geological, and remote sensing sources.

Skills & qualifications

RequiredNice to have

Skills

PythonMachine LearningExploration GeophysicsGeophysical InversionsScientific Computing

Qualifications

PhD in Geophysics or Related Field

Full job description

About Terranox We're the first AI-powered uranium discovery company. Nuclear is the only clean, scalable, and baseload energy source available today, and we’re structurally short on uranium. We're addressing that by applying AI/ML to a discovery process that hasn't fundamentally changed in decades. Backed by General Catalyst, 776 Ventures, Y Combinator, and others. The role You'll be our founding applied scientist, anchoring the scientific foundations of our discovery stack. You'll lead how we work with the full range of geoscientific data — geophysical, geochemical, geological, and remote sensing — from what we acquire, to how we process and integrate it, to how it feeds the systems that drive our targeting decisions. Much of the work has no off-the-shelf playbook. Requirements

  • PhD in geophysics, applied physics, computational physics, applied mathematics, or a closely related field; or equivalent deep technical expertise
  • Deep experience with exploration geophysics and with geophysical inversions
  • Strong scientific computing skills (Python, the standard stack)
  • Working fluency with ML — you can be a real thought partner on what we're building
  • Comfort with ambiguous, sparse, multi-modal data

Bonus

  • Experience working alongside ML researchers or engineers
  • Prior early-stage startup experience

Logistics

  • In-person in San Francisco

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