ChainOpera AI is hiring a

Web3 AI Research Scientist in Distributed/Decentralized Training of Foundation Models

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 implement novel algorithms for distributed and decentralized training of large-scale AI models

• Develop techniques to improve the efficiency, scalability, and privacy of decentralized machine learning systems

• Collaborate with blockchain experts to integrate AI training protocols with distributed ledger technologies

• Conduct cutting-edge research at the intersection of AI, distributed systems, and blockchain technology

• Publish research findings in top-tier conferences and journals

• Work closely with engineering teams to transition research prototypes into production-ready systems

• Stay abreast of the latest advancements in AI, particularly in the areas of federated learning and privacy-preserving machine learning

Requirements:

• Ph.D. in Computer Science, Machine Learning, or a related field

• Strong background in machine learning, deep learning, and distributed systems

• Experience with large-scale model training and optimization techniques

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

• Familiarity with distributed computing frameworks (e.g., Ray, Dask)

• Excellent problem-solving skills and ability to think creatively

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

Preferred Qualifications:

• Experience with blockchain technologies and decentralized systems

• Knowledge of federated learning and differential privacy techniques

• Familiarity with high-performance computing environments

• Track record of open-source contributions to AI or distributed systems projects

• Experience mentoring junior researchers or leading research projects

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