<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> <strong>// MISSION</strong>Our world model targets general-purpose robot fleets, not single units. The moment you have more than one robot sharing a space, new problems show up that don't exist at the single-robot level: avoiding each other reliably, communicating what they're doing, and — ideally — splitting up and coordinating on shared tasks instead of just staying out of each other's way. You'd define how OMN4I thinks about multi-robot behavior from the ground up.<br/><br/><strong>// WHAT YOU'LL DO</strong>_ Research and build coordination mechanisms for multiple robots operating in shared space — starting with reliable collision and conflict avoidance as the floor, not the ceiling. _ Work on inter-robot communication: what robots need to signal to each other, and how, for coordination to actually hold up outside simulation. _ Push toward collaborative multi-robot task execution — cases where robots split, sequence, or jointly execute a task rather than just operating independently in the same space. _ Connect this work to the core world model — how a shared or communicated model of the environment supports coordination, rather than treating it as a bolt-on layer. _ Validate on our real robot fleet, not just simulation — coordination failures that only show up on hardware are exactly what you're here to catch.<br/><br/><strong>// WHAT WE LOOK FOR</strong>_ Strong research background in multi-agent systems, multi-robot systems, distributed control, or multi-agent reinforcement learning — PhD or equivalent industry research experience. _ Real experience with at least one concrete piece of this: collision/conflict avoidance, decentralized or distributed coordination, inter-agent communication protocols, or task allocation across multiple robots or agents. _ Comfortable starting from "make sure they don't collide" and building up toward genuine collaboration — you don't need to have solved the hard version yet, but you should understand why the easy version isn't enough. _ Can work close to real hardware and real physical constraints, not only in simulated multi-agent environments.<br/><br/><strong>// NICE TO HAVE</strong>_ First-author publications on multi-robot systems, multi-agent RL, swarm robotics, or distributed coordination. _ Experience with heterogeneous robot teams (different embodiments coordinating together), not just identical units. _ Background connecting multi-agent coordination to learned world models or shared environment representations. _ Track record of taking multi-robot work from simulation to real hardware.<br/><br/> </div>