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