3 of 3 Federated Learning Jobs in London

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 More ❯

Start date: ASAP What you'll do: Build and deploy AI models (LLMs, NLP, image recognition, predictive analytics) into business workflows. Prototype privacy-first solutions using techniques such as federated learning, differential privacy, and secure multi-party computation. Implement safeguards across the ML lifecycle: data security, access controls, and loss-prevention policies. Design monitoring and auditing frameworks to More ❯

Start date: ASAP What you'll do: Build and deploy AI models (LLMs, NLP, image recognition, predictive analytics) into business workflows. Prototype privacy-first solutions using techniques such as federated learning, differential privacy, and secure multi-party computation. Implement safeguards across the ML lifecycle: data security, access controls, and loss-prevention policies. Design monitoring and auditing frameworks to More ❯