ChainOpera AI is hiring a

Web3 AI Research Scientist in Large-scale Distributed/Decentralized Model Inference

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 novel algorithms and architectures for distributed and decentralized inference of large-scale AI models

• Design and implement techniques to optimize inference speed, reduce latency, and improve resource utilization in decentralized environments

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

• Conduct cutting-edge research at the intersection of AI, distributed systems, and blockchain technology, with a focus on inference optimization

• Publish research findings in top-tier conferences and journals

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

• Stay current with the latest advancements in AI, particularly in the areas of model compression, quantization, and efficient inference techniques

Requirements:

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

• Strong background in machine learning, deep learning, and distributed systems, with a focus on model inference

• Experience with large-scale model deployment and optimization techniques

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

• Familiarity with distributed computing frameworks and edge computing technologies

• Excellent problem-solving skills and ability to think creatively about inference optimization

• Strong publication record in top-tier AI conferences or journals, particularly in the area of efficient model inference

Preferred Qualifications:

• Experience with blockchain technologies and decentralized systems

• Knowledge of model compression techniques, quantization, and hardware-aware neural architecture search

• Familiarity with edge AI and IoT deployments

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

• Experience mentoring junior researchers or leading research projects in the field of efficient AI inference

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