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.