We are hiring on behalf of our client who is developing a cutting-edge autonomous agent runtime focused on high-frequency financial environments. While current agents operate effectively as independent units, the next phase of evolution involves building a sophisticated intelligence layer where the entire fleet learns autonomously from real-time market outcomes.
The Staff AI Engineer will be responsible for moving beyond manual propagation of insights to a system where the fleet gets smarter with every trade. This is a high-stakes production role, not a research position. The feedback loop is immediate and measurable: the work produced either enhances agent profitability or it does not. The successful candidate will own the intelligence layer that turns thousands of daily trading decisions into compounding, autonomous growth.
Key Responsibilities:
Learning & Optimization
Feedback Loop Implementation: Design and implement systems that connect trade outcomes back to strategy improvement, specifically focusing on signal selection, risk parameters, position sizing, and timing.
Evaluation Frameworks: Build frameworks to quantify which signals and market conditions accurately predict profitable trades versus noise.
Automated Strategy Generation: Develop systems to explore new configurations, backtest them against real fleet data, and surface candidates for deployment autonomously.
Market Adaptation: Build mechanisms to detect shifts in market conditions (e.g., trending vs. choppy) and adapt fleet behavior in real-time.
Autonomous Fleet Intelligence
Fleet Monitoring: Create higher-order agents for automated monitoring to catch configuration errors and performance degradation across all concurrent agents.
Performance Attribution: Decompose trades into component drivers—signal accuracy, execution efficiency, and exit timing—to feed insights back into strategy design.
Coordination & Risk: Manage concentration risk and capital allocation across the fleet, balancing the exploration of new approaches with the exploitation of proven strategies.
Model & Inference
Infrastructure Ownership: Transition from external LLM dependence to controlled intelligence, evaluating hosting strategies ranging from proxied external models to fine-tuned, domain-specific models.
Data Capture: Build the telemetry and data capture layer to ensure every decision and outcome is structured and queryable.
Domain-Specific Training: Determine the efficacy of domain-specific training over general-purpose prompting and build the necessary pipelines for implementation.
Inference Optimization: Optimize inference for many concurrent agents, ensuring structured decision outputs and cost-efficiency at scale.