Computational Chemist, Thermodynamics (Founding Team)

Riven Systems; New York (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

Riven Systems seeks a Computational Chemist to translate thermodynamic first-principles into high-fidelity industrial predictions. You will collaborate with hydrometallurgy, process modeling, ML, and autonomous experimentation teams to build accurate thermodynamic models.

The role welcomes interdisciplinary backgrounds as we build a lean, early-stage team that thrives on exploration and rigorous scientific practice.

Qualifications

• Ph.D. in a related field or equivalent industry experience.
• Deep experience with thermodynamics modeling frameworks and tradeoffs.
• Experience regressing thermodynamic parameters against experimental data.

Responsibilities

• Own thermodynamic description of aqueous electrolyte and mineral systems.
• Design a multi-framework modeling architecture integrating ML and EoS methods.
• Build automated pipeline for regressing thermodynamic parameters from data.
• Develop DOE approaches to guide automated lab experiments.
• Connect thermodynamics into multi-scale process models.

Skills

Thermodynamics

Data regression

Interdisciplinary communication

Software architecture

Education

Ph.D. in Chemistry/Engineering/Geochemistry

Job description

About the role

As a Computational Chemist you will transform thermodynamic first-principles into high-fidelity predictions of industrial scale behavior. You will work closely with experts in hydrometallurgy, process modeling, machine learning, and autonomous experimentation to build thermodynamic models of unprecedented accuracy.

Riven is hiring a lean, interdisciplinary team and experience spanning multiple roles is welcome.

Initial Responsibilities

• Own Riven’s thermodynamic description of aqueous electrolyte and mineral systems.
• Design a multi-framework thermodynamic modeling architecture that merges ML and EoS approaches.
• Build an automated pipeline for regressing thermodynamic parameters against experimental data.
• Develop thermodynamic design-of-experiments approaches for optimally driving our automated lab.
• Connect the thermodynamics into multi-scale process models.

Qualifications

• Ph.D. in Chemistry, Chemical Engineering, Materials Science, Geochemistry, or a related field, or equivalent industry experience.
• Deep experience with solution thermodynamics in at least one framework (Pitzer, e-NRTL, MSE, SAFT, HKF, etc.), and understanding of the tradeoffs between them.
• Experience regressing thermodynamic parameters against experimental data, including critical assessment of the underlying measurements.
• Working familiarity with hydrometallurgical or comparable unit operations, and with heat and mass balance analysis.
• Comfortable with the tools and processes for building large software systems.
• Clear interdisciplinary communication and the curiosity to quickly learn and execute in new domains.
• Ability to prioritize, execute, and collaborate with a high degree of agency, a willingness to be wrong, and the ambition to build something that has never existed before.
• Trust, humor, rigor, and an excitement to work in a scrappy, interdisciplinary early-stage environment.

Bonus Points

• Background in critical minerals, mineral processing, or aqueous geochemistry.
• Experience working with f-block elements.
• Experience with process modeling and flowsheet development.
• Bayesian calibration, uncertainty quantification, or optimal experimental design.
• Experience working alongside automated or high-throughput experimentation.
• Published thermodynamic assessments, or contributions to open-source thermodynamic tooling.

We welcome candidates from diverse backgrounds who have a keen and demonstrated interest in early stage investing. We’re proud to be an equal opportunity employer and consider qualified applicants without regard to race, color, religion, sex, national origin, ancestry, age, genetic information, sexual orientation, gender identity, marital or family status, veteran status, medical condition or disability.

Compensation decisions are made based on track-record, seniority, and skillet. Additional consideration can be given to candidates with exceptional experience.