AI Multi-Robot Task Allocation Developer

Fovix Labs

Hybrid · $160k – $200k / year · Full-time · Posted 4 months ago

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About the role

Coordinating 20 robot arms in the same warehouse cell sounds like a scheduling problem until two robots try to occupy the same space, a third is blocked waiting for a handoff that has not happened, and the whole cell throughput drops to 40% of design capacity. Multi-robot task allocation is the problem of assigning tasks to robots dynamically as the world changes — machines fail, order priorities shift, and new pick requests arrive faster than any static schedule can accommodate. Our autonomous manipulation systems operate 14 warehouse cells across North America. The static scheduling approach we use today works in controlled conditions; it fails in real warehouses with human workers, forklift interference, and variable order profiles. We need a dynamic, online allocation system that replans in milliseconds. You'll design the allocation algorithm — likely a combination of RL-based policy for routine allocation and optimization-based replanning for exception handling — and implement it in a way that is safe to deploy in a live warehouse with physical robots and human workers sharing the space.

Responsibilities

Design dynamic task allocation algorithm for 20-robot warehouse cell coordination
Implement online replanning that responds to robot failures and priority changes in milliseconds
Build safety constraints ensuring collision avoidance and human-robot separation
Test allocation system in simulation before physical deployment
Deploy and validate in live warehouse environment with human workers
Monitor allocation efficiency and tune policies based on real operational data

Requirements

5+ years in robotics or autonomous systems with multi-agent planning experience
Experience with reinforcement learning, MAPF, or combinatorial optimization for task allocation
Strong Python skills and robotics middleware knowledge
Comfort with safety-critical deployment requirements in physical environments
Experience validating autonomous systems in real operational settings