Location: San Francisco, CA, United States
Job Type: Internship
Salary: $5K - $12.5K / monthly
Minimum school year: Any
Visa: Will sponsor
Skills: Machine Learning, Computer Vision, Machine learning
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 spend the fall embedded with our team, working on novel ML problems at the core of what we're building. With a strong fit, this internship can lead to a full-time offer.
Requirements
Currently pursuing a degree in CS, ML, or a related field
Strong ML fundamentals and practical experience training models
Comfortable in Python and the modern ML stack
Track record of building things (coursework, side projects, open source, prior internships)
Bonus but not required
Experience with computer vision
Experience with foundation models or self-supervised learning
Prior internship at a startup
Logistics
In-person in San Francisco
Summary
Work on novel ML problems at the core of an AI-powered uranium discovery platform during a fall internship. Requires current enrollment in CS/ML or a related degree, strong ML fundamentals, model training experience, Python proficiency, and a track record of building projects. Computer vision and foundation model experience are preferred.
Responsibilities
Spend the fall embedded with the team, working on novel ML problems at the core of the product
Qualifications
Currently pursuing a degree in CS, ML, or a related field; strong ML fundamentals and practical experience training models; comfortable in Python and the modern ML stack; track record of building things through coursework, side projects, open source, or prior internships
Education requirements
Currently pursuing a degree in CS, ML, or a related field
Experience requirements
Strong ML fundamentals and practical experience training models; track record of building things through coursework, side projects, open source, or prior internships