Software Engineer, Robot Autonomy (Multi Agent Orchestration), Intern

Job description

Our Mission At Laelaps AI, we believe robotics is entering a transformative decade, much like the arrival of the internet. Advances in AI, cloud computing, and hardware are reshaping what autonomous systems can do. Our mission is to build the intelligent software that powers physical security in the real world - enabling robots and sensors to handle dangerous and critical tasks that humans shouldn't have to. By engineering the orchestration layer for intelligent security, we aim to create a world that is safer, more secure, and more resilient. We're a strong founding team based in Zurich, backed by visionary investors and advisors. We are engineering the future of security today! The Role As a Software Engineering Intern in Multi-Agent Orchestration , you'll work on developing the orchestration layer that enables our autonomous agents to coordinate, collaborate, and make intelligent decisions to achieve efficient and scalable surveillance. Your primary focus will be on Multi-Agent Reinforcement Learning (MARL) , exploring how agents can learn optimal coordination strategies in dynamic environments. You'll also have the opportunity to explore alternative approaches, including task allocation algorithms, decentralized planning, and optimization techniques, to build robust and adaptive multi-agent systems. This role is highly practical as we expect the developed methodologies not only to work I. simulation, but be deployed in real world scenarios. What You'll Work On Multi-Agent Reinforcement Learning: Design, train, and evaluate MARL algorithms for cooperative multi-robot surveillance and persistent patrolling. Multi-Robot Coordination: Develop strategies for decentralized decision-making, task allocation, and efficient collaboration between autonomous robots. Simulation: Improve our simulations to train the policies that can be later transferred to the real world. Real-World Deployment: Build and test algorithms in simulation environments, with opportunities to deploy them on real quadruped robots. Algorithm Research & Optimization: Explore and compare learning-based approaches with classical optimization, planning, and heuristic methods. Performance Evaluation: Develop benchmarks to assess surveillance coverage, patrol efficiency, scalability, and robustness in dynamic environments. Who We're Looking For We're looking for a motivated robotics or machine lea…