Staff AI Engineer

• Design and implement the pipeline that connects live trade outcomes back to strategy improvement — signal quality, position sizing, timing, risk parameters
• Build the evaluation framework that separates genuine predictive signal from noise across agents, market conditions, and configurations
• Automate the strategy generation and testing cycle — the system should explore new configurations, validate them against real fleet data, and surface deployment candidates
• Detect regime shifts in market conditions and adapt fleet behavior accordingly
• Decompose every trade into its component drivers — signal quality, execution efficiency, exit timing — and wire those attributions back into strategy design
• Manage fleet-level coordination: concentration risk, capital allocation, and the exploration vs. exploitation balance
• Build the telemetry and data capture layer that makes all of the above possible

• Own the build-vs-buy decision on model hosting — evaluate proxied external APIs versus fine-tuned models on owned infrastructure and execute the chosen path
• Determine whether domain-specific training on trading data meaningfully outperforms prompted general-purpose models — then build the pipeline to act on that answer
• Optimize inference for the specific demands of a large autonomous agent fleet: concurrent agents, structured outputs, cost efficiency at scale
• Build the agent telemetry layer capturing every decision, signal score, and evaluation across the fleet

• A production closed-loop system — model outputs drove real-world actions, outcomes were measured, and that feedback automatically improved the next decision. Not a batch retrain. Not a dashboard with manual follow-through. A live, wired loop.
• Practical RL or online learning experience — you understand the challenges of learning from real-world feedback rather than static datasets
• Full-stack ML ownership — you build the pipeline, deploy the model, and own the outcome; Python primary, comfortable with Go or TypeScript in production services
• High-stakes sequential decision-making domain experience — finance preferred but not required; robotics, autonomous vehicles, game AI, ad bidding, and supply chain all transfer

• LLM fine-tuning and open-source model serving in production (vLLM, TGI, PEFT/LoRA)
• Multi-agent system design
• Financial ML — signal generation, execution optimization, portfolio construction
• Onchain or DeFi experience

1. Intro call with founders (60 min) — fit, motivation, your closed-loop experience
2. Technical deep-dive (60 min) — open-ended system design, no right answer, evaluating how you think
3. Paid trial project (1 week, part-time) if needed — real problem, compensated

Compensation