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Director Computational Materials Science Jobs (NOW HIRING)

SES

Computational Materials Scientist

Woburn, MA · On-site +1

This powerful combination of "AI for science" and material engineering enables batteries that can ... As a Computational Materials Scientist, you will be a core data-driven modeler responsible for ...

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 ... science to help us develop a software framework for designing and discovering new advanced ...

Alignerr

Background in computational materials science, density functional theory (DFT), or related ... Make a direct, tangible impact on how AI understands and reasons about materials science

Alignerr

$75 - $90/hr

Background in computational materials science or atomistic simulation * Familiarity with density ... Make a direct, tangible impact on how AI understands and reasons about materials science * Training ...

Amat

Computational Materials Scientist

Santa Clara, CA · On-site

$161K - $221K/yr

If you want to push the boundaries of materials science and engineering to create next generation ... Direct Help Line at 877-612-7547, option 1, and following the prompts to speak to an HR Advisor.

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Director Computational Materials Science information

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$11K

$116.2K

$176.5K

How much do director computational materials science jobs pay per year?

As of May 28, 2026, the average yearly pay for director computational materials science in the United States is $116,237.00, according to ZipRecruiter salary data. Most workers in this role earn between $77,500.00 and $151,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Director of Computational Materials Science, and why are they important?

To excel as a Director of Computational Materials Science, you need advanced expertise in materials science, computational modeling, and a PhD or equivalent experience in a related discipline. Familiarity with simulation software (such as VASP or LAMMPS), high-performance computing systems, and relevant programming languages (like Python or Fortran) is typically required, along with a track record of published research. Exceptional leadership, project management, and interdisciplinary communication skills help drive innovation and guide diverse research teams. These abilities are crucial for successfully advancing materials discovery initiatives and achieving organizational research goals.

How does a Director of Computational Materials Science typically collaborate with experimental teams and other departments?

Directors of Computational Materials Science frequently work alongside experimental scientists, engineers, and product development teams to ensure computational models align with real-world results. They facilitate regular meetings to discuss findings, guide research priorities, and integrate computational predictions into experimental design. Strong communication and interdisciplinary collaboration are essential, as directors must translate complex simulation results into actionable insights for non-specialist stakeholders. This cross-functional teamwork drives innovation and accelerates the development of new materials.

What is a Director of Computational Materials Science?

A Director of Computational Materials Science is a senior leadership role responsible for overseeing research and development efforts that use computational modeling and simulation to design and understand materials. They manage teams of scientists and engineers, set strategic research directions, and collaborate with other departments or external partners to drive innovation. This role often requires a strong background in materials science, physics, or chemistry, as well as expertise in computational methods and leadership experience. Directors in this field play a key role in advancing new materials for industries such as electronics, energy, and manufacturing.

What is the difference between Director Computational Materials Science vs Computational Materials Scientist?

AspectDirector Computational Materials ScienceComputational Materials Scientist
CredentialsAdvanced degrees (Ph.D.), leadership experienceTypically Ph.D. or Master's in materials science, physics, or related fields
Work EnvironmentLeads teams, manages projects, strategic planningConducts research, develops models, performs simulations
Employer & Industry UsageResearch institutions, large corporations, R&D divisionsAcademic labs, industry R&D, tech companies
Search & Comparison IntentLeadership roles, strategic responsibilitiesTechnical expertise, hands-on research

The main difference is that the Director Computational Materials Science oversees research teams and strategic initiatives, while the Computational Materials Scientist focuses on conducting simulations and technical research. Both roles require strong technical credentials, but the director position emphasizes leadership and project management.

More about Director Computational Materials Science jobs
What cities are hiring for Director Computational Materials Science jobs? Cities with the most Director Computational Materials Science job openings:
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Director Computational Materials Science jobs near you
Infographic showing various Director Computational Materials Science job openings in the United States as of May 2026, with employment types broken down into 6% As Needed, 6% Full Time, and 88% Part Time. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $116,237 per year, or $55.9 per hour.
Computational Materials Scientist

Computational Materials Scientist

SES

Woburn, MA • On-site, Remote

Full-time

Medical

Posted 24 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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