Job details

AI Research Scientist in Multi-modality

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 multi-modal AI systems that can effectively process and understand diverse data types
Research and implement advanced techniques for cross-modal learning, transfer, and fusion
Investigate methods for improving the robustness and generalization of multi-modal AI models
Develop innovative approaches to multi-modal representation learning and alignment
Collaborate with blockchain and distributed systems experts to explore decentralized multi-modal AI 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 with a focus on multi-modal AI
Strong background in deep learning, computer vision, natural language processing, and audio processing
Experience with multi-modal datasets and state-of-the-art multi-modal AI models
Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
Excellent problem-solving skills and ability to think creatively about multi-modal AI architectures
Strong publication record in top-tier AI conferences or journals, particularly in the field of multi-modal AI

Preferred Qualifications:

Experience with self-supervised learning techniques for multi-modal data
Knowledge of few-shot and zero-shot learning in multi-modal contexts
Familiarity with blockchain technologies and decentralized systems
Track record of open-source contributions to multi-modal AI projects
Experience mentoring junior researchers or leading research projects in AI

Tokenomics Researcher and Designer

Responsibilities:

Conduct in-depth research on existing tokenomic models and their performance in various blockchain ecosystems
Design and develop innovative tokenomic models tailored to our AI-driven blockchain projects
Collaborate with blockchain engineers and AI specialists to implement and optimize tokenomic strategies
Analyze market trends and competitor tokenomics to inform our strategic decisions
Create detailed reports and presentations on tokenomic findings and proposals
Participate in the development of whitepapers and technical documentation related to our token economy

Requirements:

Bachelor's degree in Economics, Computer Science, Mathematics, or a related field; Master's degree preferred
Proven experience in tokenomics design and implementation for blockchain projects
Strong understanding of blockchain technology, cryptocurrencies, and decentralized finance (DeFi)
Excellent analytical and problem-solving skills
Proficiency in data analysis and modeling tools
Familiarity with AI concepts and their potential integration with blockchain technology
Excellent communication skills and ability to explain complex concepts to both technical and non-technical audiences

Preferred Qualifications: