Researcher with a strong foundation in machine learning to investigate core scientific challenges of AI. Contributing to community-driven AI networks and engaging with global research community.
Responsibilities
Conduct original research on learning, distributed inference, and trustless collaboration in AI systems
Contribute to the design of incentive mechanisms that support open and community-driven AI networks
Prototype experimental systems and communicate findings internally and externally
Translate theoretical insights into practical designs with engineering collaborators
Engage with the global research community in AI, ML systems, and applied cryptography
Requirements
Background in computer science, machine learning, applied mathematics, or a related field
Demonstrated research experience in distributed ML, secure computation, federated learning, or incentive systems
Ability to write clear technical documentation and communicate complex ideas effectively
Bonus: experience with protocols, zero-knowledge proof systems, or system-level AI optimization
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