Founding AI/ML Engineer for Autonomous Process Design

Riven Systems; New York (NY); Presencial; USD 120.000 - 180.000; Jornada completa

Descripción de la vacante

Riven Systems in New York is seeking a Machine Learning Scientist to help define multi-scale modeling futures, working with thermodynamics, hydrometallurgy, and autonomous experimentation to merge ML with physical modeling.

You will build learned components of thermodynamic and process models, design active learning experiments, and develop optimization methods for flowsheet design at scale, with a strong emphasis on uncertainty quantification.

Formación

• Ph.D. in machine learning, applied mathematics, statistics, physics, chemical engineering, or 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.
• 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.

Responsabilidades

• 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.

Conocimientos

Bayesian methods

Optimization

LLM agents

Software engineering

Interdisciplinary communication

Educación

Ph.D. in machine learning / related field

Herramientas

JAX

PyTorch

Descripción del empleo

Riven Systems in New York is seeking a Machine Learning Scientist to help define multi-scale modeling futures, working with thermodynamics, hydrometallurgy, and autonomous experimentation to merge ML with physical modeling.

You will build learned components of thermodynamic and process models, design active learning experiments, and develop optimization methods for flowsheet design at scale, with a strong emphasis on uncertainty quantification.