Artificial Intelligence (AI), Artificial Intelligence (AI) Games, Capital Allocation, Continuous Improvement, Data Modeling, Fleet Management, Production Systems, Python Programming/Scripting Language, Reinforcement Learning, Risk, Robotics, Telemetry, Trading Systems, Vehicle Fleets, eLearning
About the Role
You'll build the learning system for autonomous trading agents — enabling the fleet to learn from its own decisions and continuously improve.
This is a production ML role (not research) focused on reinforcement learning and closed-loop systems.
Key Responsibilities
Learning System & RL Loop
- Build feedback loop from trade outcomes strategy improvement
- Develop evaluation frameworks for signals and performance
- Automate strategy generation and backtesting
- Detect market regime shifts and adapt strategies
- Implement performance attribution systems
- Manage fleet coordination (risk, capital allocation)
- Build telemetry/data infrastructure
Model & Inference
- Define and implement model hosting strategy
- Build domain-specific training pipelines
- Optimize inference for real-time trading agents
- Capture full decision telemetry across agents
Requirements
- Closed-loop production ML systems (non-negotiable)
- RL / online learning experience
- Full-stack ML ownership (data model production)
- Strong Python + backend experience (Go/TS helpful)
- High-stakes system experience (preferred)
Bonus Skills
- Financial ML / trading systems
- LLM fine-tuning & serving
- Multi-agent systems
- DeFi / onchain experience
- Experience in sequential decision systems (robotics, game AI, etc.)