Lead Computational Materials Scientist — AI & Physics

Xora Innovation; San Diego (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Elemynt is seeking two computational scientists to advance an AI-native materials platform for semiconductor manufacturing. You will choose methods, create workflows, run calculations, and validate predictions against fab data.

Responsibilities include modeling precursor chemistry, training MLIPs, performing MD simulations, and translating atomistic results to process outcomes for conformality and throughput. Strong Python and workflow automation are essential.

Qualifications

• PhD in chemistry, physics, materials science, chemical engineering, or closely related field.
• 5+ years of post-PhD industry experience in semiconductor materials/manufacturing or related software
• Demonstrated production use of DFT on semiconductor-relevant chemistry
• Hands-on experience with molecular dynamics and MLIP selection/validation
• Familiar with process simulation TCAD and translating atomistic results to process outcomes
• Strong Python and workflow automation skills
• Track record of predictions influencing decisions

Responsibilities

• Model precursor chemistry, surface reactions, and thin-film processes using DFT.
• Build/train MLIPs and deploy in MD and kinetic simulations.
• Run classical and reactive MD for nucleation, defects, interfaces, and plasma-surface interactions.
• Bridge atomistic results to continuum and process-level models to predict conformality and throughput.
• Design simulation campaigns for active-learning and generative models.
• Collaborate with customer process engineers to validate predictions against data.
• Establish reproducible, automated simulation infra and workflows.

Skills

Python

DFT

Workflow automation

ML for materials

Material modeling

Data analysis

Scientific communication

Education

PhD in chemistry/physics/materials science

Tools

LAMMPS

ReaxFF

VASP

Quantum ESPRESSO

ASE

pymatgen

AiiDA

FireWorks

Job description

Elemynt is seeking two computational scientists to advance an AI-native materials platform for semiconductor manufacturing. You will choose methods, create workflows, run calculations, and validate predictions against fab data.

Responsibilities include modeling precursor chemistry, training MLIPs, performing MD simulations, and translating atomistic results to process outcomes for conformality and throughput. Strong Python and workflow automation are essential.