AI Research Scientist in LLM-based Agent

ChainOpera AI is the world’s first truly decentralized and open AI platform for simple, scalable, and trustworthy collaborative AI economy, and the AI app ecosystem for accessible and democratized AI - our GPUs, our model, our personal AI.

ChainOpera AI is supported by

• Enterprise-level generative AI platform for system scalability, model performance, and security/privacy (ChainOpera AI Platform)

• Leading open source library in large-scale distributed training, model serving, and federated learning (FedML)

• Innovative and unique edge-cloud collaborative AI models and systems towards on-device personal AI (Fox LLM)

• Internet veterans for serving billion-level end users based on cloud computing and mobile internet

• Established researchers in blockchain, machine learning, and large-scale distributed systems (80000+ citations)

• Ecosystem partnership with GPU providers, model developers, AI platforms, and AI applications

• Top-tier investors, angels, and advisors

Responsibilities:

• Design and develop novel architectures for LLM-based agents that can reason, plan, and execute tasks autonomously

• Research and implement advanced techniques for improving agent capabilities, including multi-task learning, few-shot learning, and continual learning

• Investigate methods for enhancing the reliability, safety, and ethical behavior of LLM-based agents

• Develop strategies for efficient integration of external knowledge and tools with LLM agents

• Collaborate with blockchain and distributed systems experts to explore decentralized agent architectures

• Publish research findings in top-tier AI conferences and journals

• Work closely with engineering teams to prototype and deploy research outcomes

Requirements:

• Ph.D. in Computer Science, Artificial Intelligence, or a related field

• Strong background in natural language processing, deep learning, and reinforcement learning

• Experience with large language models and their applications

• Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)

• Excellent problem-solving skills and ability to think creatively about AI agent architectures

• Strong publication record in top-tier AI conferences or journals

Preferred Qualifications:

• Experience with multi-agent systems and collaborative AI

• Knowledge of cognitive architectures and symbolic AI approaches

• Familiarity with blockchain technologies and decentralized systems

• Track record of open-source contributions to AI projects, particularly in the field of language models or AI agents

• Experience mentoring junior researchers or leading research projects in AI