Job Description New York City
$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.
What you'll work on- 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
What you'll bring- 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
The company- Series A backed by Sequoia ($28M)
- Platform approaching one trillion tokens processed
- Major enterprise customers live
- Small, engineering-led team building the learning layer enterprise AI will depend on
Everyone will receive a response.
