AI Engineer – Agent Reasoning & Multimodal Dialogue at SHOPEE IP SINGAPORE PRIVATE LIMITED – Singapore | Jobyssey

AI Engineer – Agent Reasoning & Multimodal Dialogue at SHOPEE IP SINGAPORE PRIVATE LIMITED – Singapore | Jobyssey

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.