Senior Research Engineer – Large Multimodal AI Models

About the role

The S-Lab for Advanced Intelligence at NTU is seeking a Senior Research Engineer to drive the development of large multimodal and agentic AI systems. This one‑year, renewable position will work closely with principal investigators and industry partners to translate cutting‑edge research into robust prototypes and production‑ready pipelines.

Key responsibilities

• Design, implement, train, evaluate, and optimise AI agentic systems, including LLM‑based agents, tool use, retrieval‑augmented generation, planning, memory, and workflow automation.
• Lead the creation of research prototypes, software systems, datasets, tools, and experimental pipelines supporting the research programme.
• Provide technical guidance, conduct code reviews, and support junior engineers, research staff, and students.
• Collaborate with project team members to define engineering plans, milestones, experiments, and deliverables.
• Participate in technical discussions, project meetings, reviews, and demonstrations.
• Maintain high engineering standards: clean code, documentation, reproducible experiments, version control, and responsible data/computing resource management.
• Perform any additional duties relevant to the research programme.

Required profile

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, AI, Machine Learning, or a related field.
• Strong hands‑on programming experience, preferably in Python.
• Proven ability to design, implement, debug, and optimise research prototypes and production‑oriented systems.
• Excellent problem‑solving skills and the capacity to work independently in a fast‑moving research environment.
• Effective communication and collaboration skills with researchers, engineers, and students.

Required skills

• Python programming.
• Modern AI/ML frameworks (e.g., PyTorch, TensorFlow).
• Experience with large language models and multimodal models.
• Development of AI agents, workflow automation, and on‑device/edge AI deployment.
• Version control (Git), testing, documentation, and reproducible experiment practices.