Materials Science AI Engineer

Responsibilities:

• Design, develop and deploy multi-modal AI, ML, and hybrid physical-based models to solve 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.
• Collaborate with materials scientists, chemists and software engineers to integrate analytics and predictive modeling into 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 and improving consistency.

• Strong proficiency in Python and C++.
• Experience with machine learning and deep learning frameworks.
• Knowledge of generative modeling techniques and architectures.
• Knowledge of MLOps, model deployment pipelines and CI/CD.
• Experience with data cleansing, preprocessing and feature engineering.

• Graduate or undergraduate degree in Computer Science, Engineering, Applied Mathematics or a related technical field.
• 2 4 years of work experience in data science, AI, machine learning or data engineering roles.
• Strong foundation in the principles of materials science.
• Expertise in Python and data science libraries.
• Expertise in cloud-based compute environments and tools for parallel or distributed computing.
• Strong problem-solving and communication skills.