About Moonsong Labs:Entourage has been incubated by Moonsong Labs, a cutting-edge Web3 and AI venture studio driving next-wave developer and end-user adoption. Moonsong Labs creates software infrastructure and protocols at the intersection of Web3 and AI, disrupting traditional industries, empowering individuals, and fostering a more equitable digital landscape.
Recent ventures include:- Kluster.ai – Anti-hallucination platform for Machine Learning models- Moonbeam – EVM-compatible L1 blockchain optimised for cross-chain use cases- Tanssi – Decentralised AppChain infrastructure secured via Restaking
What you'll do:
- Lead the end-to-end design and development of core AI systems that enable collective learning and shared memory among autonomous agents.
- Architect and implement scalable distributed infrastructure for capturing, validating, and surfacing agent experiences across complex networks at scale..
- Drive innovation in protocol-level mechanisms for memory curation, knowledge consolidation, and token-incentivized participation across mutually distrusting agents.
- Build frameworks and tooling that allow agents to transform episodic episodic experiences and action trajectories into reusable, network-wide intelligence.
- Collaborate closely with the CTO to operationalize cutting-edge work in reinforcement learning, LLMs, and multi-agent coordination into production-grade systems.
- Define and uphold technical standards for code quality, security, reliability and scalability across the AI and protocol layers.
What you'll bring:
- Previous experience in AI architectures and infrastructure, with a proven track record of delivering complex software platforms and AI-native products.
- Proven track record of shipping production systems or prototypes at high velocity, ideally in startup or research contexts where speed and adaptability are paramount.
- Deep expertise in Generative AI, multi-agent systems, LLMs, end-to-end MLOps, and AI infrastructure. Exposure to Deep Learning, Reinforcement Learning, federated learning, AI evaluation or ML fundamentals is highly beneficial.
- Active interest and awareness of SOTA in multi-agent systems, collective learning, AI safety, secure agent execution, emerging AI agent architectures, and tool integration.
- Familiarity with one or more multi-agent frameworks (such as LangGraph, LangChain, CrewAI, AutoGen, and Pydantic AI), communication standards (such as MCP, A2A, Story’s Agent TCP/IP, Near’s AITP), distributed systems, distributed AI architectures, and consensus mechanisms.
- Ability to lead and supervise other engineers and AI scientists; this role is expected to grow into a leadership position and is not limited to individual contribution.
- Strategic thinking about platform adoption, scalable developer ecosystems, and familiarity with the Blockchain, Web3, and tokenomics (beneficial but not required).
- Postgraduate degree in a STEM field (Master’s required; PhD strongly preferred).
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