AI Researcher Intern at BioSpace in Remote

Summary: GenScript Biotech Corporation is a global biotechnology group committed to enhancing health through biotechnology. They are seeking an AI Researcher Intern who will engage in various research functions, including architecture design and implementation, memory system architecture development, and multi-agent collaboration architecture. Responsibilities: - Research and design Agent execution framework, providing standardized runtime environment for intelligent agents - Implement tool call orchestration mechanism, supporting unified abstraction for function calling, API integration, and external system interaction - Build execution sandbox environment to ensure safety and controllability of Agent operations - Design task decomposition and planning engine, supporting automatic breakdown of complex goals and execution path optimization - Implement execution state tracking and anomaly recovery mechanisms to ensure reliability of long-running tasks - Design hierarchical memory architecture, covering storage and retrieval mechanisms for working memory, short-term memory, and long-term memory - Research memory compression and summarization techniques, enabling efficient storage of massive interaction history while preserving key information - Build context-aware memory system, supporting multi-dimensional memory association based on time, task, and user - Develop memory retrieval augmentation mechanisms, achieving deep integration of RAG and Agent memory - Explore memory forgetting and update strategies, balancing memory capacity with information timeliness - Research multi-Agent system architecture, design communication protocols and collaboration mechanisms between Agents - Implement role specialization and task allocation algorithms, supporting orchestration of expert Agents, coordinator Agents, executor Agents, and other roles - Build consensus achievement and conflict resolution mechanisms to handle decision disagreements among multiple Agents - Design Agent social behavior norms, simulating communication, negotiation, and feedback patterns in human team collaboration - Explore emergent behavior and collective intelligence, researching self-organization and adaptive capabilities in multi-Agent systems - Design Agent evaluation and benchmarking system, establishing quantitative capability metrics - Build Agent behavior interpretability framework, supporting decision process tracing and attribution analysis - Research Agent safety alignment mechanisms to prevent risks such as unauthorized operations, harmful outputs, and goal drift - Track cutting-edge Agentic AI research and translate academic achievements into engineering practice Required Qualifications: - Must be currently pursuing a Master's degree or PhD in an AI related discipline - Proficient in Python, familiar with asynchronous programming, concurrency control, and performance optimization - Familiar with mainstream LLM frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, etc.) - Experience in large-scale distributed system design and implementation - Familiar with containerization technologies such as Docker and Kubernetes - Deep understanding of Transformer architecture and large model principles - Familiar with Prompt Engineering, Function Calling, Tool Use, and related technologies - Experience in RAG system development, familiar with vector retrieval, text Embedding, re-ranking, and related techniques - Understanding of reinforcement learning fundamentals; experience with RLHF, DPO, and related methods is a plus - Ability to conduct independent technical research, responsible for the entire process from problem definition to solution implementation - Strong literature reading and summarization skills, able to quickly absorb cutting-edge research achievements - Capability in technology selection and evaluation, able to make reasonable decisions among multiple solutions - Strong passion for AI technology, keeping up with the latest developments in the Agentic AI field - Excellent communication and collaboration skills, able to work efficiently with engineering teams - Critical thinking ability, capable of objectively evaluating and iteratively optimizing technical solutions Preferred Qualifications: - Experience with RLHF, DPO, and related methods is a plus Required Skills: Python, Asynchronous programming, Concurrency control, Performance optimization, LangChain, LlamaIndex, AutoGen, CrewAI, Large-scale distributed system design, Docker, Kubernetes, Transformer architecture, Large model principles, Prompt Engineering, Function Calling, RAG system development, Vector retrieval, Text Embedding, Re-ranking, Reinforcement learning fundamentals, RLHF, Technical research, Literature reading and summarization, Technology selection and evaluation