What are the responsibilities and job description for the Ai Engineer position at DCode Talent?
Job Details
Local applicants only; no sponsorship available.
Job Summary:
- Develop and deploy advanced AI, ML, and hybrid models for materials discovery and design.
- Aggregate, process, and quality-control experimental and simulation data for modeling and analysis.
- Design, optimize, and maintain scalable data workflows and pipelines on cloud platforms (Azure, Google Cloud Platform, AWS).
- Collaborate with multidisciplinary teams (materials scientists, chemists, software engineers) to integrate predictive modeling into R&D.
- Document code, workflows, and best practices for reproducible research.
- Apply AI and data analytics to optimize material synthesis and processing in real-time, improving quality and consistency.
- Build materials-informatics pipelines combining simulation (DFT/MD), experimental, and manufacturing data.
- Develop deep learning models to forecast physical and chemical properties of materials.
- Utilize strong programming skills (Python, C ), machine learning frameworks (PyTorch, TensorFlow), and cloud computing tools.
- Apply statistical theory, data preprocessing, feature engineering, and generative modeling techniques (GANs, VAEs, transformers).
- Leverage expertise in MLOps, CI/CD, and scalable model deployment.
- Require a degree in Computer Science, Engineering, Applied Math, or a related field, plus 4 years' experience in AI/data science roles.
- Must have foundational knowledge of materials science to enable impactful AI solutions.
- Develop and deploy advanced AI, ML, and hybrid models for materials discovery and design.
- Aggregate, process, and quality-control experimental and simulation data for modeling and analysis.
- Design, optimize, and maintain scalable data workflows and pipelines on cloud platforms (Azure, Google Cloud Platform, AWS).
- Collaborate with multidisciplinary teams (materials scientists, chemists, software engineers) to integrate predictive modeling into R&D.
- Document code, workflows, and best practices for reproducible research.
- Apply AI and data analytics to optimize material synthesis and processing in real-time, improving quality and consistency.
- Build materials-informatics pipelines combining simulation (DFT/MD), experimental, and manufacturing data.
- Develop deep learning models to forecast physical and chemical properties of materials.
- Utilize strong programming skills (Python, C ), machine learning frameworks (PyTorch, TensorFlow), and cloud computing tools.
- Apply statistical theory, data preprocessing, feature engineering, and generative modeling techniques (GANs, VAEs, transformers).
- Leverage expertise in MLOps, CI/CD, and scalable model deployment.
- Require a degree in Computer Science, Engineering, Applied Math, or a related field, plus 4 years' experience in AI/data science roles.
- Must have foundational knowledge of materials science to enable impactful AI solutions.
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