Senior Engineer - AI Enabled Materials Modeling

Industry: Advanced Manufacturing | Materials Science | Artificial Intelligence

Our client is seeking an experienced Senior Engineer with a strong background in either materials science and engineering or artificial intelligence
, along with an interest in expanding their knowledge of the complementary discipline.

This position combines materials engineering, multiscale modeling, scientific machine learning, and experimental validation to advance materials research and manufacturing process development.

Candidates are not expected to be experts in every technical area. The ideal individual will bring strong technical fundamentals, intellectual curiosity, a collaborative mindset, and a willingness to develop new skills. Experience with Industrial Internet of Things (IIoT) technologies is beneficial but not required.

• Develop computational models that link material composition, processing conditions, microstructure, properties, and performance across multiple scales.
• Apply scientific machine learning techniques, including Fourier Neural Operators (FNOs), Physics-Informed Kolmogorov-Arnold Networks (PIKANs), Physics-Informed Neural Networks (PINNs), and other physics-guided approaches.
• Integrate artificial intelligence with simulation methods such as DFT, molecular dynamics, CALPHAD, phase-field modeling, crystal plasticity, FEA, CFD, and reduced-order modeling
  .
• Develop AI-based surrogate models to support predictions, inverse problem solving, uncertainty quantification, and optimization of materials and manufacturing processes.
• Organize and evaluate materials data, design validation experiments, and assess model accuracy, reliability, uncertainty, and generalization.
• Convert physical principles and mechanisms into computational features, constraints, and interpretable modeling results.
• Partner with materials engineers, AI specialists, testing teams, and manufacturing professionals on multidisciplinary projects.
• Design and maintain reusable software applications, APIs, visualization tools, and computational workflows with clear documentation.
• Continue developing technical expertise across materials science and artificial intelligence through collaboration, mentoring, training, and practical project experience.
• Participate in IIoT-related data collection initiatives or digital twin projects as needed. Previous IIoT experience is not required.
• Support project planning and execution, technical proposals, reporting, presentations, and communication with internal and external stakeholders.

• Education and Experience: Master's degree or Ph.D. in Materials Science, Engineering, Computer Science, Applied Mathematics, Physics, or a related discipline, with approximately 3-5 years of relevant experience; alternatively, a bachelor's degree with 8+ years of experience.
• Materials Science Background: Experience in materials engineering, computational materials modeling, characterization, or manufacturing, combined with an interest in expanding AI and machine learning capabilities.
• Artificial Intelligence Background: Experience in machine learning, scientific computing, numerical analysis, or software engineering, with an interest in applying these skills to materials science and engineering.
• Candidates do not need equal expertise in both disciplines but should demonstrate strong learning ability, adaptability, and an interest in interdisciplinary technical work.
• For materials-focused candidates, experience with multiscale simulations or process-structure-property-performance relationships is preferred.
• For AI-focused candidates, familiarity with FNOs, PIKANs, PINNs, neural operators, differentiable programming, or physics-informed machine learning is desirable.
• Strong Python programming skills and experience with PyTorch, JAX, Tensor Flow
  , or similar machine learning frameworks. Knowledge of C++ or Fortran is an advantage.
• Solid software engineering practices, including modular development, code reviews, automated testing, technical documentation, and maintainable scientific applications.
• Familiarity with version control, CI/CD practices, container technologies, APIs, model registries, or production monitoring tools.
• Demonstrated ability to deliver technical solutions, collaborate across disciplines, communicate effectively, and engage with stakeholders in research, development, or manufacturing environments.
• Exposure to IIoT, industrial data…