AI Engineer - Learn Engine: Intelligence & Optimization

AI Engineer - Learn Engine: Intelligence & Optimization

Location: San Francisco HQ; Shanghai

Address: San Francisco, California

Employment Type: Full time

Location Type: Hybrid

Department: Engineering

Compensation: San Francisco $200K - $1M + 0.5% - 3% equity

Build the brain of an autonomous growth OS. The system you create will manage millions in ad spend and get measurably smarter with every dollar. This is the moat: every competitor has humans optimizing campaigns manually. You are building the intelligence layer that compounds. The Platform engineer creates the tools; you create the decisions. Together you build something nobody else has.

Must Have

• Has built recommendation, optimization, or decision systems where outputs improve future inputs.

• Strong statistical reasoning and experimentation judgment under noisy real-world data.

• Strong LLM orchestration or agent-system experience for reasoning over campaign context.

• Can design optimization policies, scoring systems, or automated recommendation loops.

• AI-first development workflow and ability to ship production systems quickly.

Nice to Have

• Ad-tech optimization patterns: bid management, budget allocation, ROAS optimization.

• Reinforcement learning experience.

• Hyperparameter optimization experience.

• Model fine-tuning experience.

• Experience building agent-driven automation where LLM agents take actions.

• Background in growth engineering, performance marketing, or data science.

• Experience with Mastra or similar agent orchestration framework.

What You Will Own

Own the intelligence and optimization layer of Learn Engine. Build recommendation engines for bid changes, budget reallocation, pause/boost decisions, and postback optimization. Turn SSOT campaign data into high-quality optimization guidance and closed-loop decision systems. Define how the system learns from outcomes and continuously improves campaign strategy over time. This role owns decision quality, optimization policy, and learning loops, not platform plumbing or simulator infrastructure.

Pay: $200,000.00 - $1,000,000.00 per year

Work Location: In person