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Home Based Graduate Materials Science Jobs (NOW HIRING)

MATSYS

Materials Science Engineer

Sterling, VA · On-site

The successful candidate will be hired as an Engineer IIIV based on experience and skillset. This ... Bachelors degree in Materials Science and Engineering, Materials Engineering, Chemical Engineering ...

Alignerr

$75 - $90/hr

Develop, solve, and critically review advanced materials science problems with real-world ... Based in the U.S., Canada, U.K., Australia, or New Zealand Nice to Have * Prior experience with ...

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Home Based Graduate Materials Science information

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How much do home based graduate materials science jobs pay per year?

As of May 28, 2026, the average yearly pay for home based graduate materials science in the United States is $123,973.00, according to ZipRecruiter salary data. Most workers in this role earn between $93,500.00 and $167,000.00 per year, depending on experience, location, and employer.
What are the most commonly searched types of Graduate Materials Science jobs? The most popular types of Graduate Materials Science jobs are:
Home Based Graduate Materials Science jobs near you
Materials Science Ai Engineer

Materials Science Ai Engineer

Cardinal Integrated

Santa Clara, CA • Hybrid

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Materials Science AI Engineer

Location: Santa Clara, CA - 5D Onsite Duration: 6-12+ Months Contract

Must Have Skills:

  • Strong proficiency in programming languages like Python and C++.
  • Experience with machine learning and deep learning frameworks (e.g., PyTorch, TensorFlow).
  • Experience with data cleansing, preprocessing, and feature engineering.

Good To Have Skills:

  • Design, develop and deploy multi-modal AI, ML, and hybrid physical-based models to solve ground-breaking material physics and design problems.

We are seeking an AI Scientist/Engineer to join our team in developing and supporting materials discovery and design. The ideal candidate will have strong experience building AI-based solutions for building neural network architecture, attention mechanisms, multi-modal learning, aggregating and structuring training data, statistical theory, and cloud-based compute for parallelized, scalable, and automated workflows.

Key Responsibilities:

  • Design, develop and deploy multi-modal AI, ML, and hybrid physical-based models to solve ground-breaking material physics and design problems.
  • Aggregate, process, transform and quality-control experimental and simulation data for modeling and analysis.
  • Design, develop, and maintain data workflows to support materials informatics initiatives. Optimize data pipelines and model execution on parallel cloud systems (e.g., Azure, GCP, AWS).
  • Collaborate with materials scientists, chemists, and software engineers to integrate analytics and predictive modeling into core R&D workflows.
  • Document code, workflows, and best practices to support reproducible research.
  • Apply AI and data analytics to optimize material synthesis and processing parameters in real-time, minimizing defects, improving consistency.

Technical Skills:

  • Strong proficiency in programming languages like Python and C++.
  • Experience with machine learning and deep learning frameworks (e.g., PyTorch, TensorFlow).
  • Knowledge of generative modeling techniques and architectures (e.g., GANs, VAEs, transformers).
  • Knowledge of MLOps, model deployment pipelines, and CI/CD.
  • Experience with data cleansing, preprocessing, and feature engineering.

Qualifications:

  • Graduate or undergraduate degree in Computer Science, Engineering, Applied Mathematics, or a related technical field.
  • 2-4 years of work experience (depending on educational degree) in data science, AI, machine learning, or data engineering roles.
  • A strong foundation in the principles of materials science is essential to understand the underlying science and set up meaningful problems for AI.
  • Expert in Python and data science libraries (e.g., pandas, NumPy, scikit-learn, TensorFlow or PyTorch).
  • Expertise in use of cloud-based compute environments and tools for parallel or distributed computing.
  • Strong problem-solving and communication skills.

Apply