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.