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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 ResponsibilitiesLearning 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.)