AI Researcher Intern

About GenScript

Founded in 2002 in New Jersey, GenScript Biotech Corporation accelerates innovation in biotech and healthcare by providing researchers and companies with the building blocks needed to develop groundbreaking treatments and products. Guided by its mission to Make People and Nature Healthier Through Biotechnology, and its role as a well-recognized biotechnology company, GenScript has a team of approximately 6,165 employees and has served more than 200,000 customers across over 100 countries and regions.

Job Title: AI Researcher Intern

Location United States (Remote)

We are currently seeking candidates that are bilingual in Mandarin Chinese and English. The estimated pay-rate will be $30 per hour, depending on education level.

Responsibilities will be dependent on core research function, and can be responsible for one or two of the below 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

Job Requirements

Basic Qualifications

• Must be currently pursuing a Master’s degree or PhD in an AI related discipline.

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

Research Capabilities

• Ability to conduct independent technical research, responsible for the entire process from problem definition to solution implementation
• Strong literature reading and s…