Staff AI Engineer

Staff AI Engineer | AI/Crypto Fintech | Seed Stage Confidential Client | Backed by Tier-1 Crypto VCs

Location: Americas preferred (US > Canada/LatAm) | Remote Compensation: $175,000-$250,000 USD base + 1% equity + team bonuses + pro-rata 2026 token launch participation Stage: Seed | ~20 employees

About the Company

A well-funded seed-stage startup building the next generation of autonomous trading technology. Backed by leading crypto-focused venture capital, the company has driven significant trading volume with zero paid acquisition and strong retention metrics. The founding team are crypto and onchain veterans with a prior unicorn venture. The platform is a purpose-built execution system for AI agents operating with real capital around the clock - the infrastructure, data pipelines, and runtime are already in production. You are building the intelligence layer on top of it.

The Problem You're Solving

A fleet of autonomous trading agents runs 24/7, generating a continuous stream of decisions and measurable outcomes. Right now those agents are effective but isolated - when one finds a winning pattern, a human has to carry that insight to the rest. Your job is to make that process autonomous: build the system where the fleet learns from itself and improves continuously without human intervention.

What You'll Own

Learning System & RL Loop (~70%)

• 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

Model & Inference Infrastructure (~30%)

• 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

What You Need

• 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

Nice to Have

• 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

Interview Process

Fast - target first call to offer within two weeks

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

Compensation

The base pay range for this role is $175,000 - $200,000 per year.