We are building an autonomous trading agent system where agents operate 24/7, making real decisions and generating real outcomes. We’re looking for a Staff AI Engineer to build the intelligence layer that enables the fleet to learn from trading data and continuously optimize performance.

Tasks

What You’ll Do
Build feedback loops from outcomes → strategy improvements (signals, risk, timing)
Develop evaluation frameworks to identify what drives profitable trades
Automate strategy generation, backtesting, and deployment
Design multi-agent learning and fleet coordination systems
Own ML/LLM systems end-to-end: data → model → production → measurable impact

Requirements

  • Proven ML engineering experience in production environments
    Background in reinforcement learning or online learning (closed-loop systems)
    Strong programming skills (Python required; Go/TypeScript is a plus)
    Experience building data pipelines and distributed systems
    Nice to have: fintech/trading, LLM fine-tuning, multi-agent systems

Not a Fit If

You focus only on research/papers

You primarily do prompt engineering

Benefits

Compensation

Total compensation: ~$450K+

Base: $175K–$250K

Equity + token upside
Ideal for engineers who want to build AI systems with direct, measurable impact on real-world performance.