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:

• Develop and implement novel techniques for ensuring the safety and robustness of AI systems, particularly in decentralized environments

• Research methods for alignment of AI systems with human values and intentions

• Investigate approaches to make AI systems more interpretable, transparent, and accountable

• Design and conduct experiments to test the safety and reliability of AI models, especially large language models and autonomous agents

• Collaborate with blockchain experts to address unique safety challenges in decentralized AI systems

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

• Work closely with engineering teams to implement safety measures in our AI products

• Stay abreast of the latest developments in AI safety research and contribute to the global discourse on ethical AI

Requirements:

• Ph.D. in Computer Science, Artificial Intelligence, or a related field with a focus on AI safety

• Strong background in machine learning, deep learning, and AI ethics

• Experience with formal verification methods, robustness testing, and uncertainty quantification in AI systems

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

• Excellent analytical and problem-solving skills with a keen eye for potential risks in AI systems

• Strong publication record in top-tier AI conferences or journals, particularly in the field of AI safety

Preferred Qualifications:

• Experience with blockchain technologies and understanding of their implications for AI safety

• Knowledge of game theory and mechanism design as applied to AI systems

• Familiarity with regulatory frameworks and policy discussions surrounding AI development

• Track record of open-source contributions to AI safety projects or tools

• Experience mentoring junior researchers or leading research projects in AI safet