AI Engineer

• Expanding the core learning framework that governs how agents improve
• Designing structured context and memory layers
• Building reasoning loops and feedback systems
• Creating continuous learning pipelines from live operational data
• Shipping production-grade Python systems into real deployments

• Experience building non-trivial LLM systems in production
• Designed agentic workflows involving reasoning, memory, and tool use
• Strong Python engineering and systems thinking
• Clear ownership of end-to-end AI systems

$200,000-$250,000 + equity

Define how AI agents actually learn in production.

This team is building the foundational learning framework behind enterprise AI systems.

Not prompt wrappers. Not repeated fine-tuning.

A system that formalises how work gets done and allows agents to improve continuously in real environments.

You'll design architectures that turn operational behaviour into structured, executable intelligence - making knowledge compound over time through reasoning loops, persistent memory, and human-in-the-loop feedback, without degrading performance.

You'll work directly with experienced founders and live enterprise customers on problems where reasoning, context, and workflow execution intersect.