Fleet Orchestration Engineer - Neuraverse (human)
LocationMetzingen / Riederich
Work modeon-site
Typefull-time
SenioritySenior
DepartmentSoftware Engineering
Company size1,001–5,000 people
PostedJun 10, 2026 · 4mo ago
Verified live17h ago
At a glanceSummarised by Seekless from the posting.
Multi-robot systems background — has shipped task allocation, fleet coordination, or multi-robot orchestration in production
Multi-robot task allocation algorithm fluency: auction-based, MILP, Hungarian, market-based; understanding of NP-hardness and approximation trade-offs
Distributed systems engineering: consensus, leader election, eventual consistency, fault recovery
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
Skills
Python
C++
OR-tools
Gurobi
CPLEX
ROS 2
BehaviorTree.CPP
Groot2
MoveIt2
OMPL
VDA 5050
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
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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
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Fleet traffic management beyond static geofencing: dynamic corridor allocation, intersection arbitration, shared resource pools (charging docks, narrow passages, elevators, doors), preemption rules
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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
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Fleet-level performance management: throughput dashboards, robot utilisation balancing, SLA tracking, predictive task rerouting when one robot slows down
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VDA 5050 master mode: NEURA fleet orchestrator dispatches to NEURA + third-party robots in NEURA-managed humanoid/service facilities (hospitality, healthcare, mixed-fleet demos)
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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
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Expose atomic skill primitives (MoveArmTo, Grasp, Release, Insert, etc.) as typed NodeGraph nodes — co-reviewed with platform Motion Planning Engineers
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Skill primitive API surface design: port schemas, error types, pre/postcondition contracts for third-party developers
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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
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NodeGraph node templates for cooperative-task primitives (the orchestrator’s execution vocabulary)
What we can look forward to
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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
Nice to Have
•
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