Applied AI/ML Engineer (Founding Team)

About Riven

Our mission is to leverage fundamental science and the exponential growth of computational power to revolutionize industrial chemistry. We are integrating autonomous laboratories, computational modeling, and AI to systematize the design, de-risking, and deployment of new chemical capabilities. Our work is deeply interdisciplinary and closes the loop between fundamental physics and industrial deployment.

We are starting with critical minerals because they underpin virtually every facet of modern life—including energy infrastructure, compute architecture, and defense systems—and are at the heart of a generational effort to reindustrialize the west. We intend to dramatically reduce the time and cost required to scale new mineral processing capacity.

About the role

As a Machine Learning Scientist you will help define the future of multi-scale modeling. You will work closely with experts in thermodynamics, hydrometallurgy, process modeling, and autonomous experimentation to decide how to unite modern ML and AI with rigorous physical modeling.

Riven is hiring a lean, interdisciplinary team and experience spanning multiple roles is welcome. Apply to whichever role is closest to your skillset.

Initial Responsibilities

• Build the learned components of Riven's thermodynamic and process models, and the methods for deciding where learning helps and where physical structure should hold.

• Own the optimal experimental design that drives our autonomous lab: active learning, Bayesian optimization, and information-based selection of what to measure next.

• Develop optimization methods for flowsheet design and operation, including surrogate-assisted and derivative-based approaches at scale.

• Build the agentic systems that let LLMs propose, simulate, and revise chemical process designs against our models.

• Design the evaluations that establish whether those agents are actually right.

• Quantify and propagate uncertainty across the stack, from parameters through predictions to design decisions.

Qualifications

• Ph.D. in machine learning, applied mathematics, statistics, physics, chemical engineering, or a related quantitative field, or equivalent industry experience.

• Substantial experience with Bayesian methods, Gaussian processes, or comparable approaches to modeling under uncertainty with limited data.

• Practical command of optimization, from gradient-based training through constrained nonlinear programming.

• Experience building with LLM agents, including tool use, evaluation design, and the failure modes of agentic systems.