AI Researcher (Agentic AI System Architecture)

Core Research Directions, responsible for one or two of the following areas:

Harness Architecture Design & Implementation

• →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

Memory System Architecture Development

• →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

Multi-Agent Collaboration Architecture

• →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

General Architecture Capabilities

• →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

Basic Qualifications

• Master’s degree or above in Computer Science, Artificial Intelligence, Cognitive Science, or related fields
• 3+ years of AI-related research or development experience, with hands-on experience in Agentic AI and LLM application architecture
• Publications in top-tier conferences (NeurIPS, ICML, ACL, EMNLP, etc.) are preferred

Programming & Engineering

• 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

AI Expertise

• 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

Architecture Design

• Capable of system architecture design, able to independently complete technical solution design for complex modules
• Familiar with design patterns and software engineering best practices
• Good habits in technical documentation writing

Research Capabilities

• 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

Soft Skills

• 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

• Contributions to Agent framework open-source projects (e.g., LangChain, AutoGen, LlamaIndex)
• Production-level Agent system deployment experience with 10,000+ daily task processing
• Familiarity with cognitive science and human decision-making theory, able to apply them to Agent architecture design
• Research experience in Embodied AI or robotics
• Familiarity with Graph Neural Networks, Knowledge Graphs, and related project experience
• Entrepreneurial experience or experience building AI products from 0 to 1

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