PostDoc-Computational Materials Science & Scientific Software Engineering

Merck • San Jose, CA

17h • Hybrid

About The Position

This role involves joining an MLIP-powered computational platform for semiconductor materials discovery, focusing on the intersection of computational chemistry, scientific software engineering, and modern AI. The position requires designing and implementing production-grade Python workflows that integrate first-principles calculations with machine-learned interatomic potentials to accelerate materials screening and enhance process understanding. The work includes developing end-to-end simulation pipelines, ensuring code quality through modularity, testing, and documentation. Responsibilities also cover running and analyzing DFT calculations, evaluating and deploying MLIP frameworks, implementing cheminformatics steps, operating on HPC infrastructure, translating domain expert requirements into software, and staying updated on AI tooling. Collaboration across disciplines and clear communication with experimentalists, data scientists, and external partners are essential.

Requirements

• PhD in Computational Chemistry, Quantum Chemistry, Materials Science, Physics, or a closely related field with a strong computational component.
• Solid grounding in quantum chemistry and surface science, including DFT, thermodynamics and kinetics, slab models, and periodic boundary conditions.
• Hands-on experience with DFT codes and ASE as a simulation interface.
• Demonstrated experience with MLIPs and training-data pipelines.
• Production-grade Python software engineering skills: type hints, docstrings, testing frameworks, linting, modular design, and strong version control with Git and CI/CD practices.
• Proficiency with HPC environments: job schedulers (e.g., SLURM or PBS), array jobs, containerization (e.g., Apptainer or Docker), and workflow orchestration tools.
• Basic cheminformatics skills including SMILES handling, three-dimensional conformer generation, binding-site identification, and NEB transition-state searches.
• Ability to translate research ideas into robust, well-documented code.
• Ability to work effectively at the research–engineering interface.
• Strong communication skills and a collaborative mindset.
• Comfort working across cross-disciplinary teams and with external partners.

Responsibilities

• Design and implement production-grade Python workflows connecting first-principles calculations with machine-learned interatomic potentials.
• Develop end-to-end simulation pipelines, including slab generation, adsorption energy screening, and molecular dynamics, ensuring code is modular, tested, and well-documented.
• Run and analyze DFT calculations with Quantum ESPRESSO and VASP via ASE, generating high-quality training data for MLIPs and validating results against experimental benchmarks.
• Evaluate and deploy MLIP frameworks (such as MACE or UMA), building robust training pipelines, validation protocols, and model-selection workflows.
• Implement cheminformatics steps for molecular input preparation, SMILES handling, 3D conformer generation, binding site identification, and NEB-based transition-state searches.
• Operate on HPC infrastructure with SLURM, containerization, and workflow orchestration to ensure reproducibility and scalability.
• Translate domain expert requirements into maintainable, production-grade software.
• Contribute to coding standards, reviews, and CI/CD practices.
• Stay curious about AI tooling and integrate new approaches into scientific workflows.
• Collaborate across disciplines, communicating clearly with experimentalists, data scientists, and external partners while delivering tangible software.

Benefits

• health insurance
• paid time off (PTO)
• retirement contributions

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree