Hydrify

Details

Positions: Machine Learning Research Intern
Location: Palo Alto, CA (Hybrid)
Innovation Area: Software and Data Analytics; Generation and Conversion; Materials and Chemicals
Position Type: TomKat Center-supported
Compensation: $8,500
Internship Term: 8 weeks

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Hydrify is an early-stage climate tech venture working to make geological hydrogen a commercial reality. Geologic hydrogen is a naturally occurring, low-carbon primary energy source with the potential to transform today’s hydrogen and energy systems if it can be reliably discovered and produced at scale. By combining machine learning with subsurface science, Hydrify aims to transform a largely untapped natural phenomenon into a reliable, scalable energy resource.

Responsibilities

  • Develop and experiment with ML models using large geospatial datasets
  • Integrate heterogeneous datasets (geology, geophysics, remote sensing, field data)
  • Support prospectivity modeling, prediction, and uncertainty analysis
  • Work closely with the founder on research-driven product development

Who Should Apply

  • Students interested in applying ML to climate and energy problems
  • Individuals excited by open-ended research questions and early-stage problem framing
  • People comfortable working in an ambiguous, fast-moving startup environment

Required Expertise

  • Foundation in machine learning or data science
  • Experience programming in Python
  • Familiarity with ML frameworks (e.g., PyTorch, TensorFlow, or similar)
  • Ability to work independently and iterate quickly on research ideas

Preferred Skills/Majors

  • Experience with geospatial, remote sensing, or time-series data
  • Background or interest in scientific ML, applied statistics, or uncertainty modeling
  • Majors may include: Computer Science, Electrical Engineering, Data Science, Energy Science & Engineering, Geophysics, or related fields