What are the responsibilities and job description for the AI/ML Engineer With Materials Science position at Sapear Inc?
HI,
Position : AI/ML Engineer With Materials Science Domain Exp
Location: Santa Clara, CA (Day 1 Onsite)
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.
Thank you,
|