Research Fellow / Research Engineer (Vision-Language Models)

Job Description

We are seeking a motivated Research Fellow / Research Engineer to join a research project focusing on the development of AI-powered solutions for industrial operations and maintenance (O&M).

The successful candidate will conduct research and development in Vision-Language Models (VLMs) capable of understanding operational environments, interpreting visual information, and providing context-aware assistance to operations and maintenance personnel.

Key Responsibilities

• Research, develop, and customize VLMs and multimodal foundation models capable of understanding visual scenes, recognizing objects and their relationships, and reasoning about operational situations.
• Integrate VLMs with domain-specific knowledge bases, technical manuals, maintenance procedures, and operational guidelines to enable knowledge-grounded visual reasoning.
• Collaborate and develop intelligent assistance capabilities that allow operations and maintenance personnel to interact with AI systems through natural language and visual inputs for operations and immersive training purposes.
• Prepare technical documentation, research reports, publications, and project deliverables in accordance with project milestones.

Job Requirements

• PhD degree for Research Fellow/Master degree for Research Engineer in Computer Science, Artificial Intelligence, Computer Engineering, or a related discipline.
• Knowledge of and practical experience with Vision-Language Models and computer vision technologies.
• Familiarity with modern deep learning architectures, including Transformers, Vision Transformers, and multimodal learning techniques.
• Experience in developing, training, fine-tuning, or evaluating deep learning models.
• Demonstrated experience in relevant AI research, multimodal AI development, or applied machine learning projects, will be a big advantage.
• Strong analytical, problem-solving, and programming skills and good communication skills and ability to work effectively in a multidisciplinary research team.