---
title: 'Senior/Staff Machine Learning Engineer at Terra AI'
canonical: 'https://feeny.ai/job/senior-staff-machine-learning-engineer-terra-ai-united-states-zqdv6yc45mf0'
type: 'job'
last_seen: '2026-09-10'
---

# Senior/Staff Machine Learning Engineer at Terra AI

- **Company:** Terra AI
- **Location:** United States
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-02-13
- **Last confirmed live:** 2026-09-10
- **Apply:** https://jobs.ashbyhq.com/terraai/696685a7-9225-4128-8f16-c9a9f509d473

## Job description

Terra AI is building a new category at the intersection of artificial intelligence, geoscience, and critical resource development.

As global demand for copper, lithium, nickel, rare earth elements, geothermal energy, and other strategic resources accelerates, the mining and subsurface industries face a growing challenge: traditional exploration methods remain slow, expensive, and highly uncertain. Terra AI was founded to help solve this problem by redefining how critical resources are discovered, evaluated, and developed.

By combining advanced machine learning, probabilistic modeling, and deep geoscience expertise, Terra AI helps exploration and mining companies make faster, more informed subsurface decisions with greater confidence and capital efficiency. The company’s platform integrates geological, geophysical, and drilling data into intelligent systems designed to improve targeting accuracy, accelerate discovery timelines, and reduce exploration risk.

Backed by leading investors including Khosla Ventures and working alongside strategic industry partners including Rio Tinto, Ero Copper, and Ramaco Resources, Terra AI is emerging as one of the more closely watched AI-native companies operating within the mining and critical minerals sector.

Terra AI’s mission is to define the new global standard for data-driven critical resource development — breaking the cost and time curve required to support electrification, energy security, and the global energy transition.

The company operates with a strong partnership mentality, combining technical rigor, candid communication, continual learning, and environmental stewardship to help modern exploration teams solve some of the world’s most important resource challenges.

## Role description

In the same way image generators have shown the remarkable ability to produce a diverse set of realistic pictures conditioned on a text prompt (and other inputs), we are developing a generative model that produces 3D geological models conditioned on geophysical surveys, borehole measurements, and other forms of physical observation. The outputs of the generative model capture what we know and don’t know about the state of the subsurface, allowing explorers to make maximally informed decisions about how and where to explore for critical resources.

We are looking for a talented deep learning engineer or scientist to lead the development of this model that will revolutionize decision-making in the earth subsurface for a wide range of clean energy applications.

## Role Responsibilities

- Design, train, test, and iterate on diffusion models for 3D geological models
- Design, train, test, and iterate on an approach for conditioning generation on geophysical data and other observations
- Inform the generation of synthetic data to improve model performance
- Adapt diffusion modeling approach to specific real-world projects in collaboration with project teams.

## Qualifications

Required Qualifications:

- Extensive PyTorch Experience
- Deep understanding of PyTorch, including writing custom modules, optimizing training, and debugging issues in large-scale models.
- Expertise in Developing Large Deep Learning Models from Scratch
- Proven ability to design, implement, and train complex deep learning architectures from the ground up.
- Data Curation Skills
- Hands-on experience in creating, cleaning, and maintaining high-quality datasets tailored for machine learning applications.
- Strong Software Engineering and Design Experience
- Proficient in software development best practices, including version control, testing, and code optimization.
- Familiarity with designing scalable and maintainable systems.

Nice-to-haves:

- Experience with Generative Models
- Familiarity with generative architectures, particularly diffusion models, and an emphasis on posterior sampling methods.
- Knowledge of Transformer Architectures
- Experience building and training transformers, especially in applications involving 3D data.
- Scaling Models Across Large GPU Clusters
- Expertise in parallelizing models across multiple GPUs and optimizing distributed training pipelines.
- Cloud Infrastructure Expertise
- Experience setting up, managing, and optimizing cloud environments for machine learning workloads, including provisioning resources and managing costs.

## About Terra AI

## Company Overview
- **One-liner**: Terra AI uses patented, purpose-built AI to model subsurface uncertainty and optimize drill planning for mining, geothermal, and carbon storage projects, accelerating the discovery of critical natural resources.
- **Entity Type**: Private (early-stage, founded 2023; funding stage not publicly disclosed)
- **Headquarters**: Palo Alto, California, USA (with an office in Sunnyvale, CA and remote options)
- **Founded**: 2023
- **Founders**: John Mern (CEO, Co-founder), Markus Zechner (CTO, Co-founder), Anthony Corso (Co-founder), and a General Manager of Reservoirs (Co-founder, name not publicly listed)

## Core Business
- **Primary industry**: Natural resources exploration (mining, energy, geothermal, carbon storage) powered by artificial intelligence
- **Target customers**: Mining and energy companies (B2B, enterprise), including mine developers, reservoir engineers, and investors in critical mineral supply chains
- **Mission or purpose statement**: “Accelerating exploration to meet energy transition needs” and “turn subsurface uncertainty into better decisions” to bring critical resources to markets faster

## Products & Services
- **Terra AI Platform**: A SaaS/AI platform that characterizes deposits, quantifies uncertainty, and optimizes drill planning. It answers questions like “Where should we drill to reduce the most resource uncertainty?” and “What is the possible range of net present values?”. The platform claims to deliver a 2.5x increase in resource definition vs. traditional methods and 50-60% fewer drilling meters throughout exploration.

## Market Standing
- **Valuation/Market Cap**: Not publicly available
- **Key Metric**: Total Funding – Not disclosed (company is private, no funding rounds reported in public sources)
- **Notable Investors/Partners**: Not listed in available sources (likely early-stage, no investor details public)
- **Growth Signals**: Founded in 2023 and already actively hiring across multiple senior roles (ML Engineer, Geologist, Geophysicist, Business Development, etc.). The company targets a critical bottleneck in mineral supply – post-discovery exploration – which is seeing increased demand from clean energy, defense, and AI-driven growth.

## Competitive Advantages
- **Patented, purpose-built AI** for subsurface uncertainty modeling, not generic data visualization
- **Decision-focused platform** that quantifies financial risk (e.g., net present value ranges) and reduces drilling costs by 50-60%
- **Founding team with deep domain expertise**: PhDs from Stanford in Aeronautics & Astronautics, Petroleum Engineering, and AI; experience at Kobold Metals, Boeing, and Stanford Center for AI Safety
- **Bridges academia and industry**: CTO Markus Zechner is an Adjunct Professor at Stanford; Anthony Corso is a visiting scholar at Stanford

## Strategic Focus
- Accelerate and de-risk post-discovery exploration for critical minerals, geothermal, and carbon storage
- Expand adoption among mining and energy companies to reduce the “exploration bottleneck” and boost downstream industries
- Grow the team of geoscientists, AI researchers, and engineers to build the subsurface foundation for a clean energy future
- Target both mining and reservoir (oil & gas, geothermal, carbon storage) verticals

## Why Work Here
- **Culture highlights**: Mission-driven to solve a critical climate challenge; team of AI PhDs, geophysicists, and engineers; flat structure with a focus on innovation
- **Remote/hybrid/office policy**: Mostly remote (US-based) with some roles labeled “hybrid” or “on-site” in Palo Alto/Sunnyvale. The company has two locations (Palo Alto HQ and Sunnyvale) and offers remote flexibility.
- **Notable perks** (from careers page):
  - Company-subsidized medical, dental & vision insurance
  - 401k plan with company matching
  - Flexible PTO plus company holidays
  - Annual company-wide winter break (December 24 – January 1)
  - Paid parental leave
  - One-time home-office set up stipend
  - Professional development budget (conferences, events)
  - Mental health support (if not covered by insurance)
- **Engineering culture**: Emphasis on AI/ML, scientific computing, and geoscience; open roles for Senior/Staff ML Engineers, ML Researchers, Computational Scientists, and Software Engineers

## Sources
1. [Terra AI Website](https://www.terraai.com/)
2. [Terra AI About Page](https://www.terraai.com/about)
3. [Terra AI Careers Page](https://www.terraai.com/careers)
4. [Built In Terra AI Profile](https://builtin.com/company/terra-ai)

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