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Computational Vasp Jobs (NOW HIRING)

SES

Computational Materials Scientist

Woburn, MA · On-site +1

As a Computational Materials Scientist, you will be a core data-driven modeler responsible for ... Utilize advanced simulation tools (VASP, Quantum Espresso) and data science libraries (TensorFlow ...

Mat3ra

Computational Materials Scientist

Walnut Creek, CA · On-site +1

$90K - $140K/yr

Overview We look for computational materials scientists excited about bridging the gap between ... D. from a tier-1 lab) * prior work on advanced electronic structure methods (VASP, Quantum ESPRESSO ...

Mat3ra

Computational Materials Scientist

Walnut Creek, CA · On-site

$90K - $140K/yr

Overview We look for computational materials scientists excited about bridging the gap between ... D. from a tier-1 lab) * prior work on advanced electronic structure methods (VASP, Quantum ESPRESSO ...

Worcester Polytech

Post-Doctoral Fellow

Worcester, MA · On-site

$45K - $70K/yr

The successful candidate will conduct cutting-edge research involving first-principles calculations mainly using VASP and related computational tools to investigate elevated-temperature phase ...

Mat3ra

Community Evangelist

San Francisco, CA · On-site +1

$100K - $140K/yr

MS or PhD in Engineering, Computational Chemistry, Computational Biology, Computer Science or ... VASP, Quantum ESPRESSO, Gaussian, NWChem, Siesta, or similar) is not required but is a plus ...

Mat3ra

Community Evangelist

San Francisco, CA · On-site

$100K - $140K/yr

MS or PhD in Engineering, Computational Chemistry, Computational Biology, Computer Science or ... VASP, Quantum ESPRESSO, Gaussian, NWChem, Siesta, or similar) is not required but is a plus ...

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How much do computational vasp jobs pay per hour?

As of Jun 8, 2026, the average hourly pay for computational vasp in the United States is $54.93, according to ZipRecruiter salary data. Most workers in this role earn between $46.88 and $73.56 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Computational VASP (Vienna Ab initio Simulation Package) Specialist, and why are they important?

To thrive as a Computational VASP Specialist, you need a solid background in computational physics, materials science, or chemistry, often supported by an advanced degree and experience with first-principles calculations. Proficiency in using VASP software, Linux/Unix systems, scripting languages (like Python or Bash), and high-performance computing resources is crucial. Strong problem-solving skills, attention to detail, and effective communication are essential soft skills for this role. These competencies enable accurate simulations, efficient workflow management, and clear collaboration with interdisciplinary research teams.

What is the difference between Computational Vasp vs Quantum Chemistry Software Developer?

AspectComputational VaspQuantum Chemistry Software Developer
Required CredentialsPhysics or materials science degree, experience with DFTChemistry or physics degree, programming skills in quantum chemistry
Work EnvironmentResearch labs, computational clustersResearch institutions, software development teams
Industry UsageMaterials science, condensed matter physicsPharmaceuticals, chemical research, academia

Computational Vasp focuses on simulating materials properties using density functional theory, primarily in materials science. Quantum Chemistry Software Developers create tools for molecular modeling and quantum calculations. While both roles involve quantum mechanics and programming, Vasp specialists typically work on materials simulations, whereas developers focus on software creation for quantum chemistry applications.

What is a Computational VASP specialist?

A Computational VASP specialist is an expert who uses the Vienna Ab initio Simulation Package (VASP) for performing quantum mechanical simulations of materials at the atomic scale. VASP is a widely used software tool in computational physics, chemistry, and materials science for modeling and predicting the properties of solids, surfaces, and molecules. Specialists in this field typically have a strong background in computational methods, quantum mechanics, and materials modeling, and they often work in research, development, or academic settings.

What are some common challenges faced by professionals working with VASP in computational research roles?

Professionals using VASP (Vienna Ab initio Simulation Package) in computational research often face challenges such as optimizing calculation parameters for accuracy versus computational cost, managing large datasets from simulations, and ensuring reproducibility of results. Additionally, they frequently need to collaborate closely with experimental teams to validate findings and may spend significant time troubleshooting input files or convergence issues. Staying updated with the latest software updates and computational techniques is also important to maintain efficient workflows.
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Computational Vasp jobs near you
Infographic showing various Computational Vasp job openings in the United States as of May 2026, with employment types broken down into 100% Contract. Highlights an 11% Physical, 88% Hybrid, and 1% Remote job distribution, with an average salary of $114,249 per year, or $54.9 per hour.
Computational Materials Scientist

Computational Materials Scientist

SES

Woburn, MA • On-site, Remote

Full-time

Medical

Posted 5 days ago

Job description

SES AI Corp. (NYSE: SES) is dedicated to accelerating the world's energy transition through groundbreaking material discovery and advanced battery management. We are at the forefront of revolutionizing battery creation, pioneering the integration of cutting-edge machine learning into our research and development. Our AI-enhanced, high-energy-density and high-power-density Li-Metal and Li-ion batteries are unique; they are the first in the world to utilize electrolyte materials discovered by AI. This powerful combination of "AI for science" and material engineering enables batteries that can be used across various applications, including transportation (land and air), energy storage, robotics, and drones.
To learn more about us, please visit: www.ses.ai
What We Offer:
  • A highly competitive salary and robust benefits package, including comprehensive health coverage and an attractive equity/stock options program within our NYSE-listed company.
  • The opportunity to contribute directly to a meaningful scientific project-accelerating the global energy transition-with a clear and broad public impact.
  • Work in a dynamic, collaborative, and innovative environment at the intersection of AI and material science, driving the next generation of battery technology.
  • Significant opportunities for professional growth and career development as you work alongside leading experts in AI, R&D, and engineering.
  • Access to state-of-the-art facilities and proprietary technologies are used to discover and deploy AI-enhanced battery solutions.

What we Need:
The SES AI Prometheus team isseeking an exceptional Computational Materials Scientist to combine physics-based simulation (DFT, MD, quantum modeling) with AI-assisted material prediction to generate high-quality training data and accelerate materials discovery. This role is crucial for advancing our understanding of electrochemical energy materials at the atomic level. As a Computational Materials Scientist, you will be a core data-driven modeler responsible for executing and automating complex simulations.
Essential Duties and Responsibilities:
  • Atomistic Modeling & Simulation
  • Conduct and oversee DFT (Density Functional Theory), MD (Molecular Dynamics), and QM (Quantum Mechanics) simulations of battery components, including electrolytes, coatings, and electrodes.
  • Develop and refine ML-enhanced force fields and surrogate models to accelerate simulation time scales and enable multi-scale simulation efforts.
  • Apply expertise in atomistic simulation and quantum modeling to solve key challenges in electrochemical energy materials (e.g., batteries/fuel cells).
  • AI Data Generation & Prediction
  • Generate high-quality, structured simulation data to serve as training sets for AI property prediction models and material screening modules.
  • Contribute to the development of battery domain LLM features and advanced property-prediction models.
  • Automate complex simulation workflows using strong coding practices to enhance efficiency and scalability.
  • Collaboration & Tooling
  • Collaborate with experimental teams, leveraging a hybrid computational + experimental literacy to validate models and drive design iteration.
  • Utilize advanced simulation tools (VASP, Quantum Espresso) and data science libraries (TensorFlow, Pandas) to manage and analyze large datasets.

Education and/or Experience:
  • Education: Ph.D. in Mechanical Engineering, Materials Science, Chemical Engineering, or a closely related computational/physics field.
  • Core Simulation Expertise: Deep and extensive experience in atomistic simulation and quantum modeling, including proficiency with key QM/DFT tools (VASP, Quantum Espresso) and MD simulations.
  • Domain Focus: Strong background in electrochemical energy materials and extensive computational work focused on batteries/fuel cells.
  • Coding Proficiency: Strong coding skills in Python (along with related libraries like Pandas and TensorFlow) for simulation workflow automation and data analysis.
  • ML Application: Experience in developing or utilizing ML-enhanced force fields and surrogate models for materials prediction., or equivalent practical experience.

Preferred Qualifications:
  • LLM Development: Experience in developing battery domain LLM features or property-prediction models.
  • Hybrid Skillset: Demonstrated experience working in a hybrid computational + experimental environment.
  • Tooling Diversity: Familiarity with additional data analysis tools like R, SQL, MATLAB, and time-series forecasting libraries like Prophet.
  • Target Background: Previous experience at national laboratories, XtalPi, Entalpic, or deep battery modeling groups.

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