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Computational Materials Dft Jobs (NOW HIRING)

SES

Computational Materials Scientist

Woburn, MA · On-site +1

$180K - $200K/yr

As a Computational Materials Scientist, you will be a core data-driven modeler responsible for ... Conduct and oversee DFT (Density Functional Theory), MD (Molecular Dynamics), and QM (Quantum ...

Ekcel Technologies Inc

AI HARDWARE ENGINEER

Santa Clara, CA

$143K - $189K/yr

Knowledge of computational materials methods (DFT, MD, phase-field modeling). * Additional Skills: * Familiarity with MLOps, HPC environments, and cloud deployment. * Proven experience (code repos ...

New

SES

Technical Product Manager

Woburn, MA · On-site +1

$200K - $225K/yr

Deep technical literacy in Computational Chemistry, including proficiency in DFT and MD simulations for materials analysis. * Product Management Experience: Proven experience as a Science-literate ...

Worcester Polytech

Post-Doctoral Fellow

Worcester, MA · On-site

$45K - $70K/yr

Conduct atomistic modeling of alloys, ceramics, and functional materials * Develop computational workflows for high-throughput simulations * Integrate machine learning methods with DFT-generated ...

Kanak Elite Services Inc

AI Engineer

Santa Clara, CA · On-site

Knowledge of computational materials methods (DFT, MD, phase-field modeling). Additional Skills: * Familiarity with MLOps, HPC environments, and cloud deployment. * Understanding of thermodynamics ...

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Computational Materials Dft information

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How much do computational materials dft jobs pay per hour?

As of Jun 12, 2026, the average hourly pay for computational materials dft in the United States is $16.76, according to ZipRecruiter salary data. Most workers in this role earn between $13.22 and $17.55 per hour, depending on experience, location, and employer.

What are some common challenges faced by Computational Materials DFT researchers when collaborating with experimental teams?

Computational Materials DFT researchers often collaborate closely with experimental scientists to validate and interpret simulation results. A common challenge is effectively communicating complex theoretical concepts and computational limitations to non-specialists, which is crucial for aligning expectations and designing complementary experiments. Additionally, discrepancies between simulated and experimental data can arise due to idealized computational models or limitations in available material parameters, requiring proactive troubleshooting and iterative collaboration. Building strong interdisciplinary relationships and maintaining open channels of communication are key to overcoming these challenges.

What is the difference between Computational Materials Dft vs Computational Materials Scientist?

AspectComputational Materials DftComputational Materials Scientist
CredentialsTypically requires a PhD in materials science, physics, or chemistry with expertise in DFT methodsRequires a PhD or master's in similar fields with broader computational modeling skills
Work EnvironmentPrimarily research-focused, often in academia or R&D labs, using DFT softwareResearch and development across industries, applying various computational techniques including DFT
Industry UsageUsed for detailed electronic structure calculations of materialsApplied for material design, simulation, and analysis across multiple sectors

Computational Materials Dft specialists focus on electronic structure calculations using DFT, while Computational Materials Scientists have broader roles involving multiple computational methods for material research and development.

What are Computational Materials DFT jobs?

Computational Materials DFT jobs involve using Density Functional Theory (DFT) and related computational methods to study and predict the properties of materials at the atomic and molecular level. Scientists and engineers in these roles use computer simulations to understand material behavior, design new materials, and optimize existing ones for applications in fields such as electronics, energy, and nanotechnology. These jobs typically require strong backgrounds in materials science, physics, chemistry, and programming, as well as experience with specialized simulation software.

What are the key skills and qualifications needed to thrive as a Computational Materials DFT (Density Functional Theory) Scientist, and why are they important?

To thrive as a Computational Materials DFT Scientist, you need a solid background in physics, chemistry, or materials science, with advanced knowledge of quantum mechanics and DFT principles, often supported by a graduate degree. Experience with computational tools like VASP, Quantum ESPRESSO, or CASTEP, as well as proficiency in programming languages such as Python or Fortran, is typically required. Strong analytical thinking, problem-solving abilities, and effective communication skills help you interpret results and collaborate with interdisciplinary teams. Mastery of these skills enables accurate simulations, insightful data analysis, and impactful contributions to materials research and development.
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What cities are hiring for Computational Materials Dft jobs? Cities with the most Computational Materials Dft job openings:
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Computational Materials Dft jobs near you
Infographic showing various Computational Materials Dft job openings in the United States as of June 2026, with employment types broken down into 34% Full Time, 47% Contract, and 19% Nights. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $34,865 per year, or $16.8 per hour.
Computational Materials Scientist

Computational Materials Scientist

SES

Woburn, MA • On-site, Remote

$180K - $200K/yr

Full-time

Medical

Posted 10 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.

The salary range for this position as required under applicable pay transparency laws.
Salary Range
$180,000-$200,000 USD

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