Job Description Job DescriptionAbout 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.)