Materials Science Ai Engineer (AI ScientistEngineer)

Role: Materials Science Ai Engineer (AI Scientist/Engineer)

Location Santa Clara CA
Onsite Requirement Yes
Number of days onsite 5 Days

Rate :: 55-60/hr on w2 $65-73/hr on C2C

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 modelling 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 modelling 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 modelling 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.