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Ai For Materials Jobs (NOW HIRING)

SES

Computational Materials Scientist

Woburn, MA · On-site +1

$180K - $200K/yr

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 ...

WinsAbove

$250/hr

We want AI to be safe and beneficial for our users and for society as a whole. Our team is a ... biomedical sciences, chemistry, and materials science. You'll work with incredibly ...

3M

Materials AI/ML Specialist

Maplewood, MN · On-site

Materials AI/ML Specialist Collaborate with Innovative 3Mers Around the World Choosing where to ... Using and helping evolve shared digital tools and platforms for experiment planning, data analysis ...

3M

Materials AI/ML Specialist

Maplewood, MN · On-site

Materials AI/ML Specialist Collaborate with Innovative 3Mers Around the World Choosing where to ... Using and helping evolve shared digital tools and platforms for experiment planning, data analysis ...

SES

Materials Business Unit Head

Woburn, MA · On-site +1

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 ...

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Ai For Materials information

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$38K

$100.7K

$158K

How much do ai for materials jobs pay per year?

As of Sep 8, 2026, the average yearly pay for ai for materials in the United States is $100,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $80,000.00 and $116,500.00 per year, depending on experience, location, and employer.

What is AI for Materials?

AI for Materials refers to the use of artificial intelligence and machine learning techniques to accelerate the discovery, design, and optimization of new materials. By analyzing large datasets and predicting properties, AI helps scientists identify promising materials for applications such as energy, electronics, and manufacturing. This approach significantly reduces the time and cost associated with traditional experimental methods, making materials research more efficient and innovative.

What are some typical challenges faced by professionals working in AI for Materials Science, and how can these be addressed?

Professionals in AI for Materials Science often encounter challenges such as limited high-quality data, integrating domain knowledge with machine learning models, and ensuring model interpretability for scientific insights. Collaborating closely with materials scientists and data engineers helps bridge knowledge gaps and improve dataset quality. Additionally, staying updated with the latest AI techniques and actively participating in interdisciplinary teams can enhance problem-solving and foster innovation in this rapidly evolving field.

What are the key skills and qualifications needed to thrive as an AI for Materials specialist, and why are they important?

To thrive as an AI for Materials Specialist, you need a solid background in materials science, data analysis, and proficiency in machine learning, often supported by an advanced degree in materials engineering, chemistry, or computer science. Familiarity with programming languages like Python, machine learning frameworks (e.g., TensorFlow, PyTorch), and materials databases is typically required. Strong problem-solving skills, collaboration, and effective communication are vital soft skills for interpreting complex data and working with interdisciplinary teams. These competencies enable the effective application of AI to accelerate materials discovery and innovation in research or industrial settings.

What is the difference between Ai For Materials vs Materials Scientist?

AspectAi For MaterialsMaterials Scientist
Required CredentialsTypically requires knowledge of AI, machine learning, and materials science fundamentalsRequires a degree in materials science, chemistry, or related fields, often with advanced degrees
Work EnvironmentPrimarily in tech labs, R&D centers, or software development teamsLaboratories, research institutions, or industrial settings
Industry UsageUsed in materials discovery, simulation, and optimization through AI toolsFocuses on experimental research, characterization, and development of materials

While Ai For Materials involves applying AI techniques to materials research, Materials Scientists focus on experimental and theoretical study of materials. Both roles often collaborate but differ in their core skills and work environments.

Infographic showing various Ai For Materials job openings in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $100,738 per year, or $48.4 per hour.

Computational Materials Scientist

SES

Woburn, MA • On-site, Remote

$180K - $200K/yr

Full-time

Medical

Re-posted 7 days ago

Key responsibilities

  • Conduct and oversee DFT, MD, and QM simulations of battery components including electrolytes, coatings, and electrodes.

  • Generate high-quality structured simulation data for AI property prediction models and automate simulation workflows.

  • Collaborate with experimental teams and utilize advanced simulation tools to validate models and drive design iteration.

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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