
location_on127, Lakeview Avenue, Huron Village, West Cambridge, Cambridge, Middlesex County, Massachusetts, 02140, United States
Your role will focus on building next-generation in silico multiphysics and multiscale simulation capabilities that power AI-driven scientific discovery. You will develop high-fidelity digital representations of complex physical systems spanning chemical and mechanical processes, transport phenomena, and electromagnetic behavior, integrating them into autonomous discovery and experimental pipelines.
You will work on integrating simulation methods—such as finite element modeling, computational fluid dynamics, phase-field methods, and TCAD-style transport/process modeling—into scalable, programmatic, and agent-driven systems that enable real-time digital twins, simulation-informed decision-making, and autonomous closed-loop workflows.
Work model: On-site
127, Lakeview Avenue, Huron Village, West Cambridge, Cambridge, Middlesex County, Massachusetts, 02140, United States
Cambridge, Massachusetts
Experience bridging atomistic simulations with coarse-grained, finite-element, and continuum models. Familiarity with machine learning approaches applied to physical simulations (e.g., surrogate models, neural operators, physics-informed neural networks), along with experience leveraging GPU acceleration and programmatic optimization for scalable simulations. Experience integrating simulation frameworks into digital twin systems, real-time decision environments, or closed-loop control workflows. Background applying simulation to complex materials and process domains such as thin-film deposition, micro/nano-fabrication, or reactive transport, with an understanding of processing–structure–property relationships.
Northrop Grumman • Huntsville, Alabama
Northrop Grumman • Huntsville, Alabama
Caterpillar Inc. • Lafayette, Indiana
Skills: Finite Element Modeling, Computational Fluid Dynamics, Phase-Field Methods, Tcad, Multiphysics Simulation, Python, Machine Learning, Gpu Acceleration, Digital Twins, Physics-Informed Neural Networks.
Education: PhD in Mechanical Engineering, Chemical Engineering, Aerospace Engineering, Materials Science, or related field required.
Lila Sciences operates within the Technology, Information, and Internet sectors as a specialized platform integrating artificial intelligence with scientific research. As the first scientific superintelligence platform and autonomous laboratory dedicated to life sciences, chemistry, and materials science, the organization focuses on applying AI to every stage of the scientific method. This approach aims to accelerate the development of solutions for human health and sustainability, enabling researchers to achieve results at a pace and scale previously unattainable. The company's infrastructure supports the creation of a foundational system where intelligent automation drives discovery across critical global challenges.
Browse more roles: All Lila Sciences jobs, engineering jobs on Recrutus.