Research Scientist I/II, Multiscale & Multiphysics Simulations

What You'll Be Building

• Develop and deploy robust multiphysics models across coupled domains (e.g., thermal, fluid, structural, electromagnetic, chemical), using methods such as coarse-grained, mesoscale, FEM, and CFD techniques.
• Build integrated multiscale frameworks that connect atomistic, mesoscale, and continuum representations to model materials and devices.
• Design and implement programmatic, agent-driven simulation workflows that can autonomously configure, execute, and refine simulations within closed-loop discovery workflows.
• Create scalable, GPU-accelerated simulation pipelines, data infrastructure, and interoperable APIs that connect commercial tools (e.g., COMSOL, ANSYS) and custom solvers deploying on cloud-based, high-throughput computing environments.
• Collaborate with AI, software, and automation teams to orchestrate and deploy closed-loop discovery workflows, integrating computational predictions with robotic and cloud-based laboratory platforms to enable automated experiment–simulation feedback cycles and accelerated R&D.

What You’ll Need to Succeed

• PhD in Mechanical Engineering, Chemical Engineering, Aerospace Engineering, Materials Science, or a related field.
• Extensive experience with multiphysics simulation methods and numerical algorithms, including FEM, CFD, TCAD/process simulation, mesoscale modelling, or related techniques.
• Strong foundation in coupled physical phenomena, including heat transfer, fluid dynamics, structural mechanics, mass transport, diffusion, electromagnetism, and reaction kinetics.
• Experience applying simulation to real-world systems in industrial settings such as semiconductors, chemical processing, aerospace, or materials manufacturing.
• Solid programming skills in Python and building simulation workflows, automation pipelines, or custom numerical models.

Bonus Points For

• Experience bridging atomistic simulations with one or more additional simulation domains including 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.

Compensation

We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.

About LILA

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.

Lila Sciences

Lila Sciences combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy.

Equal Employment Opportunity

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.