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Remote Density Functional Theory Scientist Jobs (NOW HIRING)

SES

Computational Materials Scientist

Woburn, MA · On-site +1

$180K - $200K/yr

  • Medical

This powerful combination of "AI for science" and material engineering enables batteries that can ... Conduct and oversee DFT (Density Functional Theory), MD (Molecular Dynamics), and QM (Quantum ...

Hitachi

Research Scientist - Theoretical Physics

Raleigh, NC · On-site +1

  • Medical

  • Retirement

  • PTO

Engineering & Science Job Schedule: Full time Remote: No Research Scientist - Theoretical Physics The Opportunity As a Research Scientist focused on theoretical physics applications for power system ...

Meridial

$8 - $65/hr

  • Medical

  • PTO

On a typical day, you will converse with the model on real-world scenarios and theoretical science ... Remote Seniority level: Mid - Senior Level

Zenlaw

Data Scientist (Remote)

New York, NY · On-site +1

As a Data Scientist, you will be working with our engineering team to model complex problems and ... functional business partners * Own your projects and use this autonomy to find creative and ...

Rebellion Defenses

Remote Data Scientist

Jackson, MS · On-site +1

$149K/yr

Collaborate with cross-functional teams to identify opportunities for leveraging data ... Proven experience in data science or a related field. * Proficiency in programming languages such ...

Swish Analytics

NFL Data Scientist

San Francisco, CA · On-site +1

$140K/yr

This position is remote from the USA. Duties: * Ideate, develop and improve machine learning and ... Expertise in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics ...

Swish Analytics

Soccer Data Scientist

San Francisco, CA · On-site +1

$130K/yr

This position is remote from the USA. Duties: * Ideate, develop and improve machine learning and ... Expertise in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics ...

DCG

Social Scientist

Baltimore, MD · Remote

Location Fully Remote; the selected candidate will work during DCG's core Eastern Standard Time ... Apply established theories, concepts, and techniques to address research questions and support ...

DCG

Social Scientist

Washington, DC · Remote

Location Fully Remote; the selected candidate will work during DCG's core Eastern Standard Time ... Apply established theories, concepts, and techniques to address research questions and support ...

Committee for Children

Research Scientist (Remote)

Seattle, WA · Remote

$84K - $100K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

The Research Scientist collaborates closely with cross-functional teams to help identify emerging ... Note: This position is remote; employees are eligible to work remotely in any of the following ...

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Remote Density Functional Theory Scientist information

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$44K

$81.5K

$151K

How much do remote density functional theory scientist jobs pay per year?

As of Aug 15, 2026, the average yearly pay for remote density functional theory scientist in the United States is $81,521.00, according to ZipRecruiter salary data. Most workers in this role earn between $59,000.00 and $99,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a remote density functional theory scientist, and why are they important?

To excel as a Remote Density Functional Theory (DFT) Scientist, you need an advanced degree in physics, chemistry, or materials science with a strong background in quantum mechanics and computational modeling. Proficiency with DFT software packages (such as VASP, Quantum ESPRESSO, or Gaussian) and experience with high-performance computing environments are typically required. Exceptional analytical thinking, problem-solving skills, and the ability to communicate complex results clearly are crucial soft skills. These competencies ensure accurate simulations, effective collaboration with research teams, and successful interpretation of computational results.

What are the key challenges of collaborating with experimental teams as a remote density functional theory scientist?

As a Remote Density Functional Theory Scientist, one common challenge is effectively communicating complex computational findings to experimental collaborators who may have different technical backgrounds. Aligning theoretical predictions with experimental results often requires frequent virtual meetings, clear documentation, and a collaborative mindset to refine models based on feedback. Additionally, time zone differences and remote work can make real-time problem-solving more challenging, so proactive communication and well-organized project management are essential for successful collaboration.

What is a remote density functional theory scientist?

A Remote Density Functional Theory (DFT) Scientist is a professional who specializes in using quantum mechanical modeling methods, particularly density functional theory, to study the electronic structure of molecules and materials. Working remotely, they perform simulations, analyze computational data, and collaborate with research teams to advance scientific understanding in fields such as chemistry, physics, and materials science. Their work often supports the design of new materials, catalysts, or drugs by predicting properties at the atomic level. Remote DFT scientists typically use specialized software and high-performance computing resources to carry out their research and communicate findings through reports or publications.
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Remote Density Functional Theory Scientist jobs near you
Infographic showing various Remote Density Functional Theory Scientist job openings in the United States as of August 2026, with employment types broken down into 4% Internship, 66% Full Time, 4% Part Time, and 26% Contract. Highlights an 100% Remote job distribution, with an average salary of $81,521 per year, or $39.2 per hour.

Computational Materials Scientist

SES

Woburn, MA • On-site, Remote

$180K - $200K/yr

Full-time

Medical

Re-posted 14 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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