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Hellyeah AI

AI Engineer — Learn Engine: Platform & Simulation

Hellyeah AI

Location
Onsite (San Francisco, California)
Employment
Full-time
Level
Mid Level
Posted 1 week ago

About the Role

Hellyeah AI is seeking an AI Engineer to invent and build novel tools like a campaign simulator and creative intelligence engine. This role focuses on creating product moats and a closed-loop optimization system that drives innovation in ad-tech.

Skills

TypeScript Python Browser Automation Simulation Infrastructure Experimentation Infrastructure Screen Capture Probabilistic Modeling Computer Vision Image Analysis LLM Orchestration Mastra Langchain Ad-tech Growth Engineering A/B Testing Product Design

Full job details

Build the tools that do not exist yet — a campaign simulator, a creative intelligence engine, an experimentation platform. This is invention work, not integration work. Every tool you build becomes a product moat that competitors cannot easily replicate. You will work alongside the intelligence engineer to create a closed-loop optimization system that gets smarter with every dollar spent.

Must Have:
Has built platform/tooling systems that unlock new capabilities for users, not just internal plumbing.

  • Strong TypeScript or Python with fast production shipping ability.

  • Experience with browser automation, screen capture, simulation, or experimentation infrastructure.

  • Strong product instincts for turning messy data and workflows into usable tools.

  • AI-first development workflow with high implementation velocity.

Nice to have:
Simulation engine / Monte Carlo / probabilistic modeling experience

  • Computer vision or image analysis (for creative intelligence)

  • Ad-tech or growth engineering background

  • A/B testing or experimentation infrastructure experience

  • Experience with Mastra, Langchain, or LLM orchestration frameworks

Own the platform and simulation layer of Learn Engine. Build the campaign simulator, experimentation infrastructure, screen-capture and creative-intelligence pipelines, and client-facing data products that let operators model outcomes before spending real money. This role owns tooling, infrastructure, simulation fidelity, and productized research surfaces — not the optimization policy itself.

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