Senior/Principal Computational Materials Scientist

Xora Innovation

. Model precursor chemistry, surface reactions, and thin-film growth and etch mechanisms using periodic and molecular DFT .

Posted 10/7/2026full-timeSan Diego • California • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in modeling precursor chemistry and surface reactions using DFT, with a strong focus on machine learning applications in molecular dynamics and process simulation. Proven ability to bridge atomistic results to continuum models and validate predictions against experimental data in semiconductor manufacturing.

Highest-signal resume keywords

PhD In Chemistry, Physics, Materials Science, Or Chemical Engineering5+ Years Post-PhD Industry ExperienceDemonstrated Production Use Of DFTStrong Python SkillsExperience Building And Automating Workflows

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Density Functional Theory (DFT)Molecular Dynamics (MD)Machine Learning Interatomic Potentials (MLIPs)Kinetic Monte Carlo ModelingProcess Simulation / TCADAutomated WorkflowsFilm NucleationDefect EvolutionPlasma-Surface InteractionSurface Reaction Mechanisms

Soft Skills

Collaboration With Process EngineersTechnical Validation SkillsProblem-Solving

Tools & Technologies

VASPQuantum ESPRESSOCP2KQuantumATKGaussianORCALAMMPSSentaurus ProcessVictory ProcessAiiDA

Industry Keywords

Semiconductor ManufacturingAtomic Layer Deposition (ALD)Atomic Layer Etching (ALE)Chemical Vapor Deposition (CVD)Thin-Film GrowthPrecursor ChemistryExperimental ValidationHPC/Cloud OrchestrationData ProvenanceMaterials Science

Tech Stack

Tools & technologies

CloudPython

About the role

Key responsibilities & impact

Model precursor chemistry, surface reactions, and thin-film growth and etch mechanisms using periodic and molecular DFT
Rank pathways for ALD, ALE, CVD, and epitaxy processes
Build, train, fine-tune, and deploy machine-learned interatomic potentials in large-scale molecular dynamics and kinetic simulations
Run classical and reactive MD using LAMMPS and ReaxFF for film nucleation, defect evolution, interface formation, and plasma–surface interaction
Bridge atomistic results into continuum and process-level models, including Sentaurus Process, Victory Process, and custom kinetic Monte Carlo
Predict conformality, selectivity, throughput, and device-relevant film properties
Design simulation campaigns supporting active-learning and generative models
Define computational methods, fidelity, and objectives
Work directly with process engineers at IDMs, foundries, OEMs, and materials suppliers to validate predictions against experimental and fab data
Establish reproducible, automated simulation infrastructure, including workflow managers, HPC/cloud orchestration, and data provenance
Select methods, build workflows, run calculations, and ensure predictions hold up against fab data

Requirements

What you’ll need

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
Hands-on experience with molecular dynamics, including force-field or MLIP selection, validation, and interpretation
Working familiarity with process simulation / TCAD (such as Sentaurus, Victory, or equivalent)
Understanding of how atomistic results translate to process outcomes
Strong Python skills
Experience building and automating workflows using ASE, pymatgen, AiiDA, FireWorks, or similar
Track record of predictions that changed an experimental or engineering decision
Preferred: experience training or fine-tuning MLIPs / universal potentials and knowing when they fail
Preferred: kinetic Monte Carlo or microkinetic modeling of surface processes
Preferred: exposure to plasma chemistry modeling (etch, PEALD)
Preferred: publications or patents in ALD/ALE mechanism, precursor design, or process modeling
Preferred: prior customer-facing technical validation in a fab or supplier setting
Preferred: detailed knowledge of semiconductor manufacturing processes and materials

Benefits

Comp & perks

Founding-stage role shaping how AI and physics-based models are used in semiconductor materials R&D
Predictions tested against real process data within weeks, not years
Freedom to choose methods and tools without legacy constraints