Strong understanding of deep learning, including CNN and Transformer.
Proven technical expertise in developing advanced analytics algorithms and models.
Strong problem-solving mindset with a continuous learning attitude.
Good awareness of data management practices and product lifecycle processes.
Proven experience in prototyping and implementing AI solutions for domain-specific problems.
Demonstrated skills in people management and effective stakeholder engagement.
Regularly following the scientific journals and publishing in scientific journals or industrial conferences.
Job Experience Requirements:
Ph.D or Masters in STEM fields: computer science, geophysics, engineering, mathematics, theoretical science, statistics, and 2+ years' work experience in deep learning technologies.
Practical experience in graduate studies or via work experience in the following areas are preferred:
Experience in the energy industry, with an understanding of typical technical challenges and industry-standard approaches
Hands-on experience designing and implementing deep neural networks, with strong intuition for trade-offs across model architectures and components
Solid knowledge of state-of-the-art Multimodal Large Language Model (LLM) architectures and algorithms
Familiarity with agentic workflows and their practical applications (e.g., AI foundry)
Experience using Git for source code management
Strong proficiency in PyTorch
Experience with distributed model training on high-performance computing (HPC) clusters, either on cloud platforms or on-premises
Experience with cloud platforms and architectures (e.g., Azure, AWS), along with comfort working in Unix-based environments (e.g., Bash)| Data Analysis, Data Engineering, Data Science
