About the role#
You will spend the fall embedded with our engineering team. You will work on novel machine learning problems that sit at the core of our uranium discovery process. This is a full-time, on-site role in San Francisco. Successful interns may receive a full-time offer.
What you'll do#
- Solve novel machine learning problems directly related to our primary technology.
- Collaborate with our engineering team on core development tasks.
- Build and train models to improve our discovery process.
What you'll need#
- Current pursuit of a degree in Computer Science, Machine Learning, or a related field.
- Strong fundamentals in machine learning and practical experience training models.
- Proficiency in Python and the modern machine learning stack.
- A track record of building things, whether through coursework, side projects, open source, or previous internships.
- Experience with computer vision, foundation models, or self-supervised learning is preferred.
- Prior experience at a startup is a plus.
Location & details#
- Location: San Francisco, California.
- Term: Fall 2026.
- Modality: On-site.
- Compensation: $5,000 - $12,500 per month.
- Sponsorship: Visa sponsorship is available.
About Terranox AI (YC W26)
Terranox AI is a mining company based in San Francisco. Founded in 2025, the firm focuses on uranium exploration. It uses physics-informed AI models to identify economic deposits across North America. The company operates with a team of eight employees.
How to get in at Terranox AI (YC W26)
Applying to Terranox AI early gives you a significant advantage before the applicant pool grows. Intern Insider sends an instant alert the moment a role matching your target is published anywhere, so you can be among the first to submit your materials. Getting your application in early ensures the team sees your profile before the pile grows too large. You can also reach out to the recruiters behind the company roles to ask about the position or a referral. Intern Insider surfaces these specific contacts, which often improves your response rates compared to applying through a general queue.
