Research Scientist

As a Research Scientist (Core AI), you will shape the theoretical and architectural foundation of how autonomous agents think, reason, and coordinate across distributed environments.

You'll work closely with platform and infrastructure engineers to translate research insights into production-grade capabilities that power the Nasiko agent layer - bridging the gap between cutting-edge theory and real-world distributed AI systems.

Responsibilities:

• Advance multi-agent intelligence - Design algorithms and models that enable autonomous agents to communicate, plan, and collaborate dynamically in open, decentralised systems.
• Develop scalable coordination frameworks - Create architectures for decentralised goal alignment, conflict resolution, negotiation, and consensus in large-scale, heterogeneous agent ecosystems.
• Enable interoperability and discovery - Design mechanisms that allow agents, services, and infrastructure components to discover each other, share capabilities, and coordinate without centralised control.
• Conduct original research in multi-agent systems, autonomous coordination, collective intelligence, and learning in distributed settings.
• Design and prototype reasoning, communication, and negotiation frameworks for large-scale agent ecosystems.
• Develop models, protocols, and architectures that enhance agent interoperability, verifiability, and adaptivity.
• Collaborate with engineering teams to operationalise research outputs, integrating them into Nasiko's core platform.
• Publish high-quality research and contribute to shaping industry standards for AI coordination.

Requirements:

• PhD (or equivalent research experience) in Computer Science, Machine Learning, Artificial Intelligence, Robotics, or a related field.
• Strong foundation in one or more of the following areas: Machine learning, deep learning, reinforcement learning, or planning algorithms, Graph-based learning and relational reasoning, Multi-agent systems and coordination, Optimisation and distributed algorithms.
• Demonstrated track record of high-quality research, including publications in top-tier venues (e. g., NeurIPS, ICML, ICLR, AAMAS, CoRL, AAAI, IJCAI).
• Proficiency in Python and deep learning frameworks (PyTorch, JAX, etc. )
• Strong systems-thinking ability with a balance of theoretical depth and practical implementation skills.
• Excellent problem-solving, communication, and collaboration abilities.

Skills

AAMAS, Deep Learning, JAX, Machine Learning, Multi-Agent, Optimization, Planning, PyTorch, Python, Reinforcement Learning