Python / PyTorch
Department:
AI Agent Research Center
Location:
Hong Kong, Shen Zhen
Work Experience:
Graduate / Early Career
Number of openings:
10
About the Role
We are seeking Multi-Agent Systems & Coordination Engineers to join our Agent Core team.
You will help design and implement systems where multiple autonomous agents:
Share state and communicate
Negotiate resources and objectives
Resolve conflicts
Execute distributed decisions
Form stable long-term cooperation strategies
This is multi-agent engineering in real industrial environments — not simulated grids.
Focus
Agent Architecture
Design agent roles (pricing, inventory, fulfillment, channel, planning)
Implement agent lifecycle management (start, pause, recovery, reconfiguration)
Define responsibility boundaries and permissions
Coordination & Decision Layer
Build inter-agent communication protocols
Implement conflict detection and negotiation frameworks
Support hybrid centralized + decentralized decision making
Apply multi-agent reinforcement learning to optimize global outcomes
Enable joint planning across agents
Systems Engineering
Build multi-agent simulation and replay environments
Develop observability tools for coordination behaviors and decision chains
Implement A/B frameworks for evaluating coordination strategies
Enable fast onboarding of new agents into existing ecosystems
Ideal Experience
Experience with complex systems, distributed systems, or agent architectures
Strong Python
Understanding of reinforcement learning or game theory fundamentals
Ability to decompose complex domains into autonomous components
Strong system-level abstraction skills
Nice to have
Multi-Agent RL
Game theory / mechanism design
Scheduling or resource allocation systems
Enterprise-scale system architecture
Typical Problems You’ll Work On
Pricing agents increase margin but cause inventory buildup — how should agents negotiate?
Fulfillment agents optimize delivery speed at higher cost — how do agents learn balanced strategies?
A new business agent is introduced — how does it integrate into the existing coordination network?
Local agent optimization degrades global performance — how do we design global incentives?
Enterprise strategy shifts (growth → profitability) — how do agents rapidly realign?
Python / PyTorch
Distributed systems
Multi-agent frameworks
TypeScript / React (internal tools)
#J-18808-Ljbffr
or continue with email
Department:
AI Agent Research Center
Location:
Hong Kong, Shen Zhen
Work Experience:
Graduate / Early Career
Number of openings:
10
About the Role
We are seeking Multi-Agent Systems & Coordination Engineers to join our Agent Core team.
You will help design and implement systems where multiple autonomous agents:
Share state and communicate
Negotiate resources and objectives
Resolve conflicts
Execute distributed decisions
Form stable long-term cooperation strategies
This is multi-agent engineering in real industrial environments — not simulated grids.
Focus
Agent Architecture
Design agent roles (pricing, inventory, fulfillment, channel, planning)
Implement agent lifecycle management (start, pause, recovery, reconfiguration)
Define responsibility boundaries and permissions
Coordination & Decision Layer
Build inter-agent communication protocols
Implement conflict detection and negotiation frameworks
Support hybrid centralized + decentralized decision making
Apply multi-agent reinforcement learning to optimize global outcomes
Enable joint planning across agents
Systems Engineering
Build multi-agent simulation and replay environments
Develop observability tools for coordination behaviors and decision chains
Implement A/B frameworks for evaluating coordination strategies
Enable fast onboarding of new agents into existing ecosystems
Ideal Experience
Experience with complex systems, distributed systems, or agent architectures
Strong Python
Understanding of reinforcement learning or game theory fundamentals
Ability to decompose complex domains into autonomous components
Strong system-level abstraction skills
Nice to have
Multi-Agent RL
Game theory / mechanism design
Scheduling or resource allocation systems
Enterprise-scale system architecture
Typical Problems You’ll Work On
Pricing agents increase margin but cause inventory buildup — how should agents negotiate?
Fulfillment agents optimize delivery speed at higher cost — how do agents learn balanced strategies?
A new business agent is introduced — how does it integrate into the existing coordination network?
Local agent optimization degrades global performance — how do we design global incentives?
Enterprise strategy shifts (growth → profitability) — how do agents rapidly realign?
Python / PyTorch
Distributed systems
Multi-agent frameworks
TypeScript / React (internal tools)
#J-18808-Ljbffr
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