Research Scientist Core AI

Nasiko

Nasiko is building the intelligent coordination layer for AI agents – a system that enables autonomous agents, data services, and infrastructure components to discover, register, and interact seamlessly. Our mission is to create an open, self-organizing AI ecosystem where agents can reason, collaborate, and transact without centralized control or human micromanagement. At the heart of this vision lies the Nasiko Registry: a shared substrate for observability, execution, and interoperability across distributed AI systems. We operate at the intersection of multi-agent intelligence, system orchestration, and adaptive infrastructure – building the primitives that make AI systems composable, transparent, and intelligent by design.

Role Overview

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

• Advance multi-agent intelligence – Design algorithms and models that enable autonomous agents to communicate, plan, and collaborate dynamically in open, decentralized systems
• Develop scalable coordination frameworks – Create architectures for decentralized 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 centralized control 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.

Key Responsibilities

• 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 operationalize research outputs, integrating them into Nasiko's core platform
• Publish high-quality research and contribute to shaping industry standards for AI coordination

What We're Looking For

Required:

• 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
• Optimization 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

Nice to Have:

• Experience with agent frameworks, simulation environments, or large-scale system prototyping
• Background in distributed systems or protocol design
• Prior experience translating research into production systems

Why Nasiko

• Define a new AI frontier – Join a small, world-class team building the foundation of the agentic internet
• Work across boundaries – Bridge research and systems engineering, transforming theoretical ideas into scalable, networked intelligence
• Shape foundational AI coordination – Tackle some of the most challenging problems in multi-agent reasoning, interoperability, and dynamic system orchestration
• Competitive compensation and equity – Your work directly shapes the future of distributed AI
• Intellectual freedom and deep focus – Work on groundbreaking problems that push beyond incremental innovation

Must-have skills

Reinforcement learningGenerative AI

Good-to-have skills

Agent frameworksSimulation environments