VP of Engineering

Terra AI•Redwood City, CA

1d•$300,000 - $400,000•Hybrid

About The Position

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 Khosla Ventures, BHP, and Rio Tinto, Terra AI works with industry leaders to bring a more informed, data-driven approach to critical resource development. 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.

Requirements

Demonstrated experience personally maturing novel technical, algorithmic, or scientific capabilities from research or early prototype into robust engineered systems deployed in real-world environments
A track record of building a meaningful engineering function, including recruiting, team design, management practices, technical processes, and developing engineering leaders
Deep software and systems architecture judgment across backend services, computational systems, interfaces, modularity, performance, reliability, and maintainability
Experience working effectively at the boundary between research scientists or advanced algorithm teams and product engineering organizations
Strong AI/ML fluency, including probabilistic outputs, model limitations, training and inference constraints, and the practical challenges of deploying novel models; a research doctorate is not required
Evidence of helping a technical capability and its supporting organization evolve through early versions rather than joining only after the system was mature
Executive-level cross-functional communication and judgment, with the ability to collaborate credibly with Research, Product, Platform, customers, and technical partners
Strong talent judgment and experience hiring engineers across ML systems, scientific computing, algorithmic software, data-intensive backend systems, or related disciplines
A pragmatic, low-ego approach to ambiguity, technical quality, collaboration, and ownership

Nice To Haves

Experience with technically novel scientific or physical-world systems—such as robotics, autonomy, simulation, computational biology, scientific AI, or advanced modeling—is strongly preferred

Responsibilities

Build Terra AI’s technology-development organization, including early hiring, team design, leadership development, operating cadence, and clear interfaces with Research and Product
Own software and systems architecture for research-derived capabilities, including backend decomposition, APIs, data flows, abstractions, interfaces, and reusable technical components
Create a repeatable research-to-engineering maturation process with clear readiness criteria, technical milestones, validation, testing, documentation, and release standards
Establish engineering rigor in benchmarking, automated testing, observability, failure-mode characterization, versioning, reliability, and maintainability
Make pragmatic technical and economic tradeoffs across model quality, latency, compute, data requirements, deployment complexity, scalability, and ongoing operating cost
Determine which capabilities should become reusable platform primitives and which should remain project-specific, avoiding unnecessary bespoke systems
Translate uncertain research results into executable engineering programs with owners, dependencies, risks, milestones, and technical exit criteria without imposing false certainty on R&D
Partner closely with research leaders to surface engineering constraints, inform research priorities, and identify the advances that unlock the most valuable next capabilities
Partner with Product and Platform to define clear technical contracts and delivery standards so engineered capabilities can be integrated into enterprise products without rebuilding their foundations
Remain a hands-on technical executive through architecture reviews, design decisions, technical mentorship, and engineering standards while building a durable leadership bench