ABOUT THE ROLEElemynt 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 FORRequired- 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.
