AI/ML Engineer With Materials Science Domain Exp

About

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, Google Cloud Platform, 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/Education

• 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.