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Remote Computational Material Science Jobs in Boston, MA

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 ... As a Computational Materials Scientist, you will be a core data-driven modeler responsible for ...

SES

Technical Product Manager

Woburn, MA · On-site +1

$200K - $225K/yr

This role requires deep literacy in computational materials science and AI4Science to coordinate strategy across our research, engineering, and materials teams. As the Technical Product Manager, you ...

Varsity Tutors

AP Computer Science A Tutor

Lynn, MA · Remote

$18 - $40/hr

Emphasizes developing computational thinking and problem decomposition skills while connecting ... Ability to identify concepts students commonly struggle with, explain material using multiple ...

Varsity Tutors

AP Computer Science A Tutor

Lowell, MA · Remote

$18 - $40/hr

Emphasizes developing computational thinking and problem decomposition skills while connecting ... Ability to identify concepts students commonly struggle with, explain material using multiple ...

Varsity Tutors

AP Computer Science A Tutor

Waltham, MA · Remote

$18 - $40/hr

Emphasizes developing computational thinking and problem decomposition skills while connecting ... Ability to identify concepts students commonly struggle with, explain material using multiple ...

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All JobsComputational Material Science JobsRemote Computational Material Science JobsRemote Computational Material Science Jobs in Boston, MA

Remote Computational Material Science information

See Boston, MA salary details

$61.4K

$90.3K

$106.5K

How much do remote computational material science jobs pay per year?

As of Aug 1, 2026, the average yearly pay for remote computational material science in Boston, MA is $90,289.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,200.00 and $101,600.00 per year, depending on experience, location, and employer.

What is the difference between Remote Computational Material Science vs Remote Materials Data Analyst?

AspectRemote Computational Material ScienceRemote Materials Data Analyst
Required CredentialsAdvanced degrees in materials science, physics, or chemistry; programming skillsBachelor's or master's in materials science, data analysis, or related fields; proficiency in data tools
Work EnvironmentResearch labs, universities, or corporate R&D teams; focus on simulations and modelingData-focused roles in corporate or research settings; emphasis on data interpretation and visualization
Industry UsageResearch institutions, tech companies, manufacturingManufacturing, tech firms, consulting
Common Search/ComparisonYesYes

Remote Computational Material Science involves using simulations and modeling to understand material properties, requiring advanced technical skills. In contrast, Remote Materials Data Analysts focus on analyzing and visualizing data related to materials, often with less emphasis on modeling. Both roles are vital in materials research and industry, but they differ in technical focus and daily tasks.

What are the most commonly searched types of Computational Material Science jobs in Boston, MA? The most popular types of Computational Material Science jobs in Boston, MA are:
What are popular job titles related to Remote Computational Material Science jobs in Boston, MA? For Remote Computational Material Science jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Remote Computational Material Science jobs in Boston, MA look for? The top searched job categories for Remote Computational Material Science jobs in Boston, MA are:
Remote Computational Material Science jobs near you

Computational Materials Scientist

SES

Woburn, MA • On-site, Remote

$180K - $200K/yr

Full-time

Medical

Re-posted 29 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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