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Computational Material Science Jobs in Massachusetts

OPT Industries, Inc.

Computational Design

Medford, MA · On-site

$80K - $120K/yr

Working at the intersection of automation engineering, computational design and polymer science, we design and commercially manufacture highly customizable materials and products- everything from ...

OPT Industries, Inc.

Computational Design

Medford, MA · On-site

Working at the intersection of automation engineering, computational design and polymer science, we design and commercially manufacture highly customizable materials and products- everything from ...

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All JobsComputational Material Science JobsComputational Material Science Jobs in Massachusetts

Computational Material Science information

See Massachusetts salary details

$24.1K

$83K

$165.7K

How much do computational material science jobs pay per year?

As of Sep 7, 2026, the average yearly pay for computational material science in Massachusetts is $83,017.00, according to ZipRecruiter salary data. Most workers in this role earn between $45,056.00 and $108,350.00 per year, depending on experience, location, and employer.

What is a computational material science?

A Computational Material Science job involves using computer simulations, modeling techniques, and data analysis to study and predict the properties of materials. Professionals in this field leverage methods like density functional theory (DFT), molecular dynamics, and machine learning to design new materials and optimize existing ones for various applications, including electronics, energy, and manufacturing. They often work in academia, research institutions, or industries such as aerospace, semiconductors, and pharmaceuticals. The role requires expertise in materials science, physics, chemistry, and programming, typically using tools like Python, MATLAB, or specialized simulation software.

What are typical daily tasks for a computational material science professional?

Daily tasks for a Computational Material Science professional often include developing and running computer simulations to investigate material properties, analyzing data from these simulations, and collaborating with experimental scientists to compare computational predictions with laboratory results. You may spend significant time programming, writing reports, and presenting your findings to colleagues or industry partners. You'll typically work within a multidisciplinary team, where clear communication and project coordination are crucial. The balance between independent computational work and collaborative meetings helps ensure innovative solutions to complex material challenges.

What are the key skills and qualifications needed to thrive in computational material science?

To thrive in Computational Material Science, you need a strong background in materials science, physics, and computational modeling, usually supported by an advanced degree such as a Master's or Ph.D. Proficiency with simulation software (like VASP, LAMMPS, or Quantum ESPRESSO), high-performance computing environments, and programming languages like Python or C++ is often required. Strong analytical thinking, problem-solving ability, and effective teamwork and communication skills help set professionals apart in this field. These skills are essential for designing, analyzing, and optimizing materials using computational techniques, often as part of collaborative, interdisciplinary research teams.

What jobs can I get with a computational material science degree?

A computational material science degree prepares individuals for roles such as materials scientist, computational researcher, or simulation engineer. These jobs often involve using modeling software, programming skills, and knowledge of materials properties to develop new materials or improve existing ones in industries like aerospace, electronics, and energy. Additional certifications or experience with tools like MATLAB, Python, or molecular dynamics software can enhance job prospects.

What are popular job titles related to Computational Material Science jobs in Massachusetts?

For Computational Material Science jobs in Massachusetts, the most frequently searched job titles are:

What job categories do people searching Computational Material Science jobs in Massachusetts look for?

The top searched job categories for Computational Material Science jobs in Massachusetts are:

What cities in Massachusetts are hiring for Computational Material Science jobs?

Cities in Massachusetts with the most Computational Material Science job openings:

Computational Material Science jobs near you
Infographic showing various Computational Material Science job openings in Massachusetts as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 22% Part Time, and 3% Contract. Highlights an 75% Physical, 4% Hybrid, and 21% Remote job distribution, with an average salary of $83,017 per year, or $39.9 per hour.

Computational Scientist I/II, Soft Matter Formulations , Complex Fluids

Lila Sciences

Cambridge, MA • On-site

Full-time

Medical, Dental, Vision, Life

Re-posted 15 days ago

Job description

Your Impact at LILA
Lila Sciences is seeking a Computational Scientist I/II, Soft Matter Formulations - Complex Fluids to develop models, tools, and workflows that accelerate discovery across liquid and flowable soft material systems. This role focuses on complex fluids, including colloidal suspensions, emulsions, surfactant systems, polymer solutions, coolants and heat-transfer fluids, coatings, inks, and lubricants.
You will bring domain expertise in soft matter, complex fluids, colloids, rheology, interfacial science, formulation science, or a closely related area, and apply machine learning methods to connect composition, microstructure, processing conditions, and bulk fluid properties. The work spans rheology and flow behavior, phase stability, dispersion and aggregation, sedimentation, shelf-life, interfacial and wetting behavior, surface tension, foaming, and thermophysical performance.
This is a hands-on scientific ML role for someone who can bridge domain context and computational execution. You will develop structure-property models linking composition to microstructure and bulk fluid behavior, build active learning workflows over continuous compositional spaces, and incorporate mesoscale or continuum simulation coupling, such as coarse-grained molecular dynamics, dissipative particle dynamics, or CFD hooks, where it improves prediction and experimental decision-making.
What You'll Be Building
  • Develop machine learning models for complex fluid systems, including colloidal suspensions, emulsions, surfactant systems, polymer solutions, coolants and heat-transfer fluids, coatings, inks, and lubricants.
  • Define modeling targets for rheology, phase stability, dispersion and aggregation behavior, sedimentation, shelf-life, and thermophysical performance for liquid formulation systems,
  • Build structure-property models that connect composition, microstructure, processing conditions, and bulk fluid properties.
  • Design active learning workflows over continuous compositional spaces that prioritize high-value experiments and formulation decisions.
  • Incorporate mesoscale and continuum simulation outputs, such as coarse-grained MD, dissipative particle dynamics, or CFD-linked features, where they improve prediction or interpretation.
  • Create tools that help scientists interpret complex fluid data and prioritize formulation, processing, or composition decisions.
  • Partner with experimental teams to align models with measurement workflows, formulation workcell throughput, material performance requirements, and practical development needs.
  • Communicate model behavior, uncertainty, and recommendations to scientific, engineering, and cross-functional collaborators.

What You'll Need to Succeed
  • Experience applying machine learning to scientific, materials-focused, complex fluid, soft matter, or formulation problems.
  • Domain expertise in colloids, emulsions, surfactants, polymer solutions, rheology, interfacial science, thermophysical fluids, coatings, inks, lubricants, or related fields.
  • Familiarity with rheology, phase stability, dispersion, aggregation, sedimentation, wetting, surface tension, foaming, thermal conductivity, heat capacity, or related fluid performance properties.
  • Strong Python skills and experience with modern ML frameworks.
  • Experience training, evaluating, and improving models using experimental, simulation, or scientific datasets.
  • Ability to use simulations, theory, descriptors, or mechanistic understanding to inform modeling choices for complex fluid systems.
  • Strong communication skills with experimental, computational, and cross-functional collaborators.
  • PhD in chemical engineering, materials science, physics, applied mathematics, computational science, or a related field, or a master's degree with equivalent relevant experience.

Bonus Points For
  • Experience working with experimental data from colloidal suspensions, emulsions, surfactant systems, polymer solutions, coolants, coatings, inks, lubricants, or related liquid formulations.
  • Experience modeling composition-to-microstructure-to-property relationships for liquid or flowable soft material systems.
  • Familiarity with active learning over continuous compositional spaces or high-throughput formulation campaigns.
  • Experience incorporating mesoscale or continuum simulation outputs, including coarse-grained MD, dissipative particle dynamics, CFD-linked models, or related approaches, into ML workflows.
  • Experience modeling thermophysical fluid properties relevant to coolant or heat-transfer applications.
  • Hands-on experimental experience in complex fluids, colloids, emulsions, rheology, interfacial science, or soft material formulation domains.

Compensation
We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.
U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.
International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.
Expected Base Salary Range
$118,800-$187,000 USD
About LILA
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
We're All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.
A Note to Agencies
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.

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