Research Scientist - Multi-Robot Coordination

MISSION

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.

• WHAT YOU\'LL DO
• 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.
• WHAT WE LOOK FOR
• Strong research background in multi‑agent systems, multi‑robot systems, distributed control, or multi‑agent reinforcement learning — Ph D 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.
• NICE TO HAVE
• 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.
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