AI Engineer Agent Reasoning & Multimodal Dialogue

Job Description:

• Design and develop core agent algorithms, including multi-turn dialogue planning, tool orchestration (retrieval, ranking, LLM synthesis), and adaptive task planning.

• Build and maintain agent memory architectures using knowledge graphs to support long-term consistency, personalization, and context retention across sessions.

• Develop emotion-aware dialogue modeling techniques to improve agent naturalness, consistency, and user engagement.

• Design and implement LLM alignment and safety strategies (e.g., SFT, DPO) to mitigate risks such as implicit persuasion or psychological manipulation in personalized generation.

• Build multimodal agent capabilities that integrate vision-language reasoning with dialogue planning for tasks such as tutoring or adaptive guidance.

• Benchmark and deploy LLMs/VLMs across GPU clusters and cloud environments (e.g., AWS) build reproducible evaluation pipelines to support model and architecture selection.

• Track frontier research in agent algorithms, contribute to publications, and represent findings at top-tier AI/NLP venues.
  

Requirements:

• Master's degree or above in Computer Science, Natural Language Processing, Artificial Intelligence, or a related field.

• Minimum 3 years of hands-on research and engineering full-time working experience building conversational agents with knowledge-graph-based memory systems, persona-aware and emotion-aware dialogue modeling, and LLM alignment techniques (SFT and DPO) for safety and behavior control, combined with experience orchestrating agent pipelines involving retrieval, ranking, and tool use.

• First-author publication(s) at top-tier venues (ACL/AAAI/EMNLP/ICLR) on persona-driven dialogue generation and persona attribute extraction, particularly methods that improve dialogue consistency and personalization quality.

• Demonstrated experience building vision-language tutoring/dialogue agents that integrate multimodal reasoning with adaptive dialogue planning, applying reinforcement learning (e.g., Deep Q-Networks) to sequential decision-making problems, and developing end-to-end 3D reconstruction pipelines (segmentation, planar extraction, geometric reconstruction) from point cloud data.

• Good programming skills in Python and Bash proficient in PyTorch and Hugging Face familiar with LoRA/PEFT, prompt engineering, and model evaluation pipelines.

• Experience benchmarking and deploying LLMs/VLMs across GPU clusters and cloud platforms (e.g., AWS EC2) familiar with data systems such as PostgreSQL, Neo4j, and AWS S3.

• Good problem analysis and research skills sustained curiosity in frontier AI/agent research able to work independently and collaboratively across research and engineering teams.