Fleet Orchestration Engineer - Neuraverse (human)

Your Mission \& Challenges
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• Multi\-robot task allocation algorithms: auction\-based (CBBA, market\-based), MILP solvers for offline planning, Hungarian for simple bipartite matching, robot\-capability matching, deadline\-aware scheduling, priority queuing


• Mission decomposition: facility\-level goal ("deliver 50 trays from kitchen to floor 3 by 18:00") per\-robot behaviour\-tree skill bundles \+ cross\-robot dependency graph \+ handoff point definitions


• Fleet traffic management beyond static geofencing: dynamic corridor allocation, intersection arbitration, shared resource pools (charging docks, narrow passages, elevators, doors), preemption rules


• Cooperative\-task primitives: synchronised place\-pick handoffs (Robot A places, Robot B picks at coordinated time), coordinated bimanual across two physical robots holding the same object, leader/follower formations, multi\-robot peg\-in\-hole


• Fleet\-level performance management: throughput dashboards, robot utilisation balancing, SLA tracking, predictive task rerouting when one robot slows down


• VDA 5050 master mode: NEURA fleet orchestrator dispatches to NEURA \+ third\-party robots in NEURA\-managed humanoid/service facilities (hospitality, healthcare, mixed\-fleet demos)


• VDA 5050 client interop testing: confirm NEURA robots integrate cleanly with third\-party fleet management systems (Open\-RMF, Otto, MiR, Symovo) for industrial deployments — protocol layer owned by Robot Connectivity Engineer


• Expose atomic skill primitives (MoveArmTo, Grasp, Release, Insert, etc.) as typed NodeGraph nodes — co\-reviewed with platform Motion Planning Engineers


• Skill primitive API surface design: port schemas, error types, pre/postcondition contracts for third\-party developers


• Pre\-deployment behaviour\-tree simulation in the Cognitive Twin: simulating full skill trees before on\-robot deployment — Digital Twin \& Multi\-Sim Engineer provides the simulation infrastructure


• NodeGraph node templates for cooperative\-task primitives (the orchestrator's execution vocabulary)

• Multi\-robot systems background as the primary axis — has shipped task allocation, fleet coordination, or multi\-robot orchestration in production. Looks like Open\-RMF contributors, AMR fleet engineers (Locus Robotics, Otto Motors, MiR fleet, Symbotic, Geek\+, Boston Dynamics Spot coordinator), or industrial automation FMS engineers (Dematic, Vanderlande, Swisslog)


• Multi\-robot task allocation algorithm fluency: auction\-based, MILP, Hungarian, market\-based; understanding of NP\-hardness and approximation trade\-offs in MRTA problems


• Distributed systems engineering: consensus, leader election, eventual consistency, fault recovery — the orchestrator is a distributed system at heart


• VDA 5050 (v2\.0\+) protocol\-level fluency: order, instantAction, state, visualization messages; master and client mode


• BehaviorTree.CPP v4 and Groot2 proficiency for the NodeGraph SDK side of the role


• Python and C\+\+; comfort with optimisation libraries (OR\-tools, Gurobi, CPLEX) for algorithm prototyping

• Academic background in multi\-robot systems: RSS/ICRA/IROS publications in MRTA, multi\-robot motion planning, swarm robotics, or fleet coordination


• Open\-RMF maintainer or significant contributor — fleet\_adapter authoring, dispatcher implementation, RMF Demos


• Cooperative manipulation experience: bimanual across two physical robots, multi\-robot assembly, leader/follower


• Per\-customer FMS adapter experience: writing integrations to plug NEURA robots into existing third\-party fleet managers (Otto, MiR, Locus, Symovo)


• Motion planning familiarity (MoveIt2, OMPL, or equivalent) sufficient to evaluate planner behaviour in pre\-deployment simulation


• Formal verification or model checking for behaviour tree skill trees


• ROS 2 (Jazzy or Humble) — useful for ROS 2\-based fleet tooling integration, but not a prerequisite for this role; multi\-robot systems expertise and distributed systems depth matter more

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