Senior / Principal Computational Materials Scientist (Xora Portfolio Company)
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Before you apply
SalaryNo pay range posted
LocationSan Diego, California, United States
Typefull-time
SeniorityLead
Experience5+ yrs
DepartmentXVL
Company size10+ people
PostedOct 6, 2026 · 1mo ago
Verified live2d ago
At a glanceSummarised by Seekless from the posting.
PhD in chemistry, physics, materials science, chemical engineering, or a closely related field
5+ years of post-PhD industry experience at a semiconductor IDM, foundry, equipment OEM, materials or precursor supplier, or an EDA / molecular-modeling software company
Demonstrated production use of DFT on semiconductor-relevant chemistry
Hands-on experience with molecular dynamics, including force-field or MLIP selection, validation, and interpretation
Working familiarity with process simulation / TCAD and understanding of how atomistic results translate to process outcomes
Strong Python; comfortable building and automating workflows (ASE, pymatgen, AiiDA, FireWorks, or similar)
Track record of predictions that changed an experimental or engineering decision
Experience training or fine-tuning MLIPs / universal potentials and knowing when they fail
Kinetic Monte Carlo or microkinetic modeling of surface processes
Exposure to plasma chemistry modeling (etch, PEALD)
Publications or patents in ALD/ALE mechanism, precursor design, or process modeling
Prior work with customer-facing technical validation in a fab or supplier setting
Detailed knowledge of semiconductor manufacturing processes and materials
Skills
Python
DFT
VASP
Quantum ESPRESSO
CP2K
QuantumATK
Gaussian
ORCA
LAMMPS
ReaxFF
Sentaurus Process
Victory Process
ASE
pymatgen
AiiDA
FireWorks
AI
machine learning
ABOUT THE ROLE
Elemynt is building an AI-native platform for discovering and qualifying materials and precursors for advanced semiconductor manufacturing. We are hiring two computational scientists to leverage our physics-based simulation stack — from atomistic chemistry through process-scale behavior — to fuse it with machine-learned models in a way that shortens the path from candidate molecule to qualified process in semiconductor manufacturing. This is a hands-on technical role in a small team. You will pick the methods, build the workflows, run the calculations, and be accountable for whether the predictions hold up against fab data.
WHAT YOU WILL DO
•
Model precursor chemistry, surface reactions, and thin-film growth and etch mechanisms using DFT (periodic and molecular) ranking pathways for ALD, ALE, CVD, and epitaxy processes.
•
Build, train, and fine tune machine-learned interatomic potentials on DFT data and deploy them in large-scale MD and kinetic simulations.
•
Run classical and reactive MD (LAMMPS, ReaxFF) for film nucleation, defect evolution, interface formation, and plasma–surface interaction.
•
Bridge atomistic results into continuum and process-level models (for example; Sentaurus Process, Victory Process, custom kinetic Monte Carlo) to predict conformality, selectivity, throughput, and device-relevant film properties.
•
Design simulation campaigns that feed active-learning and generative models — you will define what is computed, at what fidelity, and why.
•
Work directly with customer process engineers at IDMs, foundries, OEMs, and materials suppliers to validate predictions against experimental and fab data.
•
Establish reproducible, automated simulation infrastructure (workflow managers, HPC/cloud orchestration, data provenance).
WHAT WE ARE LOOKING FOR
Required
•
PhD in chemistry, physics, materials science, chemical engineering, or a closely related field.
•
5+ years of post-PhD industry experience at a semiconductor IDM, foundry, equipment OEM, materials or precursor supplier, or an EDA / molecular-modeling software company.
•
Demonstrated production use of DFT (VASP, Quantum ESPRESSO, CP2K, QuantumATK, Gaussian, ORCA, or equivalent) on semiconductor-relevant chemistry — deposition, etch, doping, interfaces, or defects.
•
Hands-on experience with molecular dynamics, including force-field or MLIP selection, validation, and interpretation.
•
Working familiarity with process simulation / TCAD (for example; Sentaurus, Victory, or equivalent) and an understanding of how atomistic results translate to process outcomes.
•
Strong Python; comfortable building and automating workflows (ASE, pymatgen, AiiDA, FireWorks, or similar).
•
Track record of predictions that changed an experimental or engineering decision.
Strongly preferred
•
Experience training or fine-tuning MLIPs / universal potentials and knowing when they fail.
•
Kinetic Monte Carlo or microkinetic modeling of surface processes.
•
Exposure to plasma chemistry modeling (etch, PEALD).
•
Publications or patents in ALD/ALE mechanism, precursor design, or process modeling.
•
Prior work with customer-facing technical validation in a fab or supplier setting.
•
Detailed knowledge of semiconductor manufacturing processes and materials.
What makes this different
•
A founding-stage role shaping how AI and physics-based models are used in semiconductor materials R&D.
•
You will see your predictions tested against real process data within weeks, not years.
•
Freedom to choose methods and tools without legacy constraints.