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Summer Remote Materials Science Jobs in Massachusetts

Entegris

Head of Entegris Ventures

Billerica, MA · On-site +1

... IL, or Remote- California. The role: The Head of Entegris Ventures (Corporate Venture Capital ... Masters degree in Technology, Materials Science or Chemical Engineering, complemented with MBA; Or ...

Entegris

Head of Entegris Ventures

Billerica, MA · On-site +1

... IL, or Remote- California. The role: The Head of Entegris Ventures (Corporate Venture Capital ... Masters degree in Technology, Materials Science or Chemical Engineering, complemented with MBA; Or ...

Vrtx

HEOR Modeling Director (Remote-Eligible)

Boston, MA · On-site +1

$204K - $306K/yr

... materials. The work may involve both in-house "hands-on" development of models (de novo programming ... Present research findings at scientific congresses, participate in development of high-quality ...

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What is the difference between Summer Remote Materials Science vs Summer Remote Chemical Engineering?

AspectSummer Remote Materials ScienceSummer Remote Chemical Engineering
Required CredentialsBachelor's or Master's in Materials Science, related certificationsBachelor's or Master's in Chemical Engineering, related certifications
Work EnvironmentResearch labs, manufacturing, academia, remote data analysisProcess design, chemical process simulation, remote lab work
Industry UsageElectronics, aerospace, biomaterialsPetrochemical, pharmaceuticals, process industries

Summer Remote Materials Science and Summer Remote Chemical Engineering share similar credentials and remote work settings but focus on different industries and applications. Materials Science emphasizes developing new materials and nanotechnology, while Chemical Engineering centers on process optimization and chemical production. Both roles are popular for remote internships in STEM fields, but their industry focus and specific skill sets differ.

What are popular job titles related to Summer Remote Materials Science jobs in Massachusetts? For Summer Remote Materials Science jobs in Massachusetts, the most frequently searched job titles are:
What job categories do people searching Summer Remote Materials Science jobs in Massachusetts look for? The top searched job categories for Summer Remote Materials Science jobs in Massachusetts are:
What cities in Massachusetts are hiring for Summer Remote Materials Science jobs? Cities in Massachusetts with the most Summer Remote Materials Science job openings:
Summer Remote Materials Science jobs near you
Infographic showing various Summer Remote Materials Science job openings in Massachusetts as of May 2026, with employment types broken down into 78% Full Time, 19% Part Time, 1% Temporary, and 2% Contract. Highlights an 34% Physical, 3% Hybrid, and 63% Remote job distribution.

AI Residency Program, Material Science (2026 Cohort)

Lila Sciences

Cambridge, MA • On-site, Remote

Other

Posted 13 hours ago

Job description

AI Resident - 2026 Cohort

The AI Residency Program is a full-time research opportunity designed to bridge the gap between academic research and industry applications in AI for materials science. Residents will work closely with Lila scientists and engineers on high-impact, open-science projects, with the option to focus on either fundamental or applied research.

  • Duration: 6-12 months (extension possible)
  • Start Dates: First hires beginning January 2026, with rolling applications and additional intakes in Summer and Fall 2026
  • Cohort Size: Small group of selected residents
  • Mentorship: Pairing with technical mentors, feedback from cross-functional teams
  • Resources: Access to proprietary datasets, high-performance compute, and Lila's research infrastructure

Research areas include ML-accelerated simulations, Bayesian methods, representation learning, generative models, agentic science, and ML-driven automation.

 
Application Requirement:
Please submit your resume alongside a research proposal (up to 3 pages, unlimited references) outlining the project you would plan to pursue during your residency at Lila Sciences. Please submit your research proposal as your cover letter. Applications without both documents will not be considered. Optional supporting materials (e.g., recommendation letters, publications, research artifacts) may also be included. 

Your Impact at Lila

The Lila Sciences AI Residency is a full-time research program at the intersection of artificial intelligence and materials science. As a resident, you'll join a cohort of researchers tackling open-ended scientific challenges alongside Lila's world-class team of scientists and engineers. With access to proprietary datasets, high-performance compute infrastructure, and experienced mentors, you'll pursue ambitious research projects with both academic and real-world impact. Publishing is encouraged but not required - what matters most is pushing the frontier of scientific discovery.

What You'll Be Building

  • Design and execute independent research projects in AI for materials science
  • Collaborate with Lila scientists and engineers on cutting-edge, open-science initiatives
  • Explore domains such as ML-accelerated simulations, Bayesian methods, representation learning, generative AI, agentic science, and ML-driven automation
  • Contribute to collaborative team research and co-develop novel approaches to scientific discovery
  • Share findings internally and externally; publications are welcome but not mandatory

What You'll Need to Succeed

  • Degree in Materials Science, Chemistry, Computer Science, AI/ML, Physics, Mathematics, or related field (Bachelor's, Master's, or PhD)
  • Proficiency in Python and deep learning frameworks (e.g., PyTorch)
  • Experience working with large-scale datasets or simulations
  • Familiarity with modern AI/ML architectures and training techniques
  • Strong research background, demonstrated through publications, thesis work, or open-source projects

Bonus Points For

  • Prior work on ML applications in scientific domains (e.g., materials discovery, chemistry, simulations)
  • Familiarity with Bayesian optimization, active learning, or generative models
  • Experience in reinforcement learning or agent-based approaches to scientific reasoning
  • Open-source contributions or collaborative research experience
  • Strong communication and writing skills, especially for conveying complex scientific ideas

Apply