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MLOpsOpenkyber • CA, United States
MLOps

MLOps

Openkyber • CA, United States
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Materials Science Ai Engineer at Santa Clara, CA 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

  • 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.
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