Position: Materials Science Ai Engineer
Location: Santa Clara, CA **Day 1 Onsite
**Duration: 1 Years
Mandatory Areas
Must Have Skills
Skill 1 Strong proficiency in programming languages like Python and C++.
Skill 2 Experience with machine learning and deep learning frameworks (e.g., PyTorch, TensorFlow).
Skill 3 Experience with data cleansing, preprocessing, and feature engineering
Good To have Skills
Skill 1 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.