Location: San Francisco, CA, United States
Job Type: Full-time
Salary: $150K - $250K
Equity: 0.10% - 1.00%
Experience: Any (new grads ok)
Visa: US citizen/visa only
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 building the ML systems at the core of Terranox. The work spans multiple modalities and model classes, much of it without an off-the-shelf playbook. You'll train computer vision models from scratch on messy, sparsely-labeled data and ship them into a production pipeline that drives real-world decisions. This is a hands-on, build-from-zero role.
Requirements
3+ years of ML experience, with a track record of building ML systems from scratch
Strong computer vision background
Experience taking ML systems to production
Comfort framing ambiguous problems, not just solving well-defined ones
Fluency with recent ML research and the ability to translate it into shipped systems
Bonus but not required
Geospatial experience
Background in sparse-label or weak-supervision regimes
Prior early-stage startup experience
Logistics
In-person in San Francisco
Summary
Build ML systems at the core of Terranox, training computer vision models from scratch on messy, sparsely-labeled data and shipping them into a production pipeline driving real-world decisions. Requires 3+ years of ML experience, strong computer vision background, and experience taking ML systems to production.
Responsibilities
Building ML systems at the core of Terranox; training computer vision models from scratch on messy, sparsely-labeled data; shipping them into a production pipeline driving real-world decisions
Qualifications
3+ years of ML experience, with a track record of building ML systems from scratch; strong computer vision background; experience taking ML systems to production; comfort framing ambiguous problems; fluency with recent ML research and ability to translate it into shipped systems
Experience requirements
3+ years of ML experience