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Remote Density Functional Theory Jobs (NOW HIRING)

SES

Computational Materials Scientist

Woburn, MA · On-site +1

Conduct and oversee DFT (Density Functional Theory), MD (Molecular Dynamics), and QM (Quantum Mechanics) simulations of battery components, including electrolytes, coatings, and electrodes. * Develop ...

Code and Theory

Executive Producer (US)

New York, NY · On-site +1

$140K - $185K/yr

... and Theory. As the cornerstone of our teams, producers are the glue that binds cross-functional ... With a remote-first approach to our people, we have teams distributed across North America, South ...

Jobgether

$76K - $96K/yr

The work spans both theoretical exploration and practical implementation, with direct applications ... Fully remote work setup within a globally distributed, research-focused team (CET-aligned ...

Planted Solar

Principal, Data Center Development

Oakland, CA · On-site +1

$160K - $230K/yr

Principal, Data Center Development Oakland, CA or Remote Full-time Energy is the binding constraint ... The core mandate is to make Planted a critical infrastructure partner for low-cost, high-density ...

Nebius

Data Center Design Lead

$90K - $220K/yr

Remote, US Responsibilities: Design Leadership • Own the end-to-end design process for new data ... Cross-Functional Collaboration • Coordinate with construction project managers and external ...

Hewlett Packard Enterprise

$60.25 - $79.50/hr

Presales HPC&AI Solutions Architect This role has been designated as 'Remote/Teleworker', which ... Provides expertise and partnership to functional and technical project teams and may participate in ...

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Remote Density Functional Theory information

What is the difference between Remote Density Functional Theory vs Remote Quantum Chemist?

AspectRemote Density Functional TheoryRemote Quantum Chemist
Required CredentialsDegree in Chemistry, Physics, or related field; knowledge of DFT methodsDegree in Chemistry, Physics, or related field; expertise in quantum chemistry techniques
Work EnvironmentResearch labs, academic institutions, or industry R&D teams, often remoteResearch-focused roles in labs or industry, frequently remote
Industry UsageMaterial science, computational chemistry, pharmaceuticalsPharmaceuticals, materials, chemical industries

Remote Density Functional Theory specialists focus on applying DFT methods to computational problems, while Remote Quantum Chemists have broader expertise in quantum chemistry techniques. Both roles often work remotely in research settings, sharing similar credentials and industry applications, but differ in specific technical focus.

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

To thrive as a Remote Density Functional Theory Researcher, you need a solid background in quantum chemistry or condensed matter physics, typically supported by an advanced degree (MSc or PhD) and experience in computational modeling. Proficiency with DFT software packages (such as VASP, Quantum ESPRESSO, or Gaussian), scripting languages (like Python or Bash), and familiarity with Linux systems are essential. Strong analytical thinking, attention to detail, and effective remote communication skills help you collaborate and troubleshoot complex scientific problems. These skills ensure accurate simulations, effective data analysis, and productive teamwork in remote or distributed research environments.

What is a Remote Density Functional Theory (DFT) job?

A Remote Density Functional Theory (DFT) job typically involves performing computational simulations and calculations using DFT methods to study the electronic structure of atoms, molecules, and materials. These roles are often found in academic, industrial, or research settings, and the work can be done entirely online using specialized software and high-performance computing resources. Tasks may include setting up simulations, analyzing electronic properties, optimizing structures, and collaborating with teams remotely. Strong knowledge in quantum chemistry, physics, and programming is usually required. The remote nature of the job allows professionals to contribute from anywhere in the world while accessing necessary computational tools online.

What are some common challenges faced by remote Density Functional Theory (DFT) researchers, and how can they be addressed?

Remote DFT researchers often encounter challenges such as limited access to high-performance computing resources, difficulty in collaborative problem-solving, and occasional isolation from peer support. These can be addressed by leveraging cloud-based computational platforms, participating in regular virtual meetings with research teams, and staying active on professional forums or collaborative platforms like GitHub. Building strong communication channels and scheduling regular check-ins with colleagues can also help maintain productivity and foster a sense of community despite the remote setting.
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Remote Density Functional Theory jobs near you
Infographic showing various Remote Density Functional Theory job openings in the United States as of May 2026, with employment types broken down into 11% Internship, 78% Full Time, and 11% Part Time. Highlights an 100% Remote job distribution.
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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