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Atomionics is building a large-scale planet model powered by proprietary quantum gravimetry technology to solve critical mineral exploration challenges. The company aims to address the global need for 500% more metal production over the next decade—metals like copper, cobalt, nickel, and lithium essential for infrastructure. With current drilling success rates at only 11%, Atomionics is leveraging advanced ML and AI to predict mineral locations and model Earth's geology.
You will lead the creation of a new ML team in the San Francisco office, spearheading efforts to build comprehensive natural resource models. Key projects include developing an agentic geologist capable of understanding text and map data, building predictive models for precious mineral locations, creating simulations of geological history, and extending classical gravity inversion models to multimodal approaches.
As AI/ML Lead, you will be hands-on when needed, guiding the team to build novel models, define North Star metrics, and design loss functions. You'll work with terabytes of unstructured geospatial and gravity data, deciding when to apply deep learning versus statistical methods suited to scarce data scenarios. The role requires bridging classical physics-based approaches with modern AI techniques.
You bring 10+ years of ML experience across industry and/or academia, with 3+ years managing teams of 5+ ML engineers. Deep expertise in deep learning and LLMs is essential, alongside broad knowledge of classical ML, Bayesian methods, and statistical foundations. A quantitative advanced degree (MS/PhD in computer science, statistics, physics, or electrical engineering) is preferred. You excel at mentoring, cross-functional communication, and thrive in ambiguous, novel problem spaces. Experience with physics, geospatial modeling, computational simulation, or information retrieval is a plus. Backed by BHP Ventures and In-Q-Tel.
