- Salary
- $200k – $250k
- Location
- US
- Type
- Full-time
- Department
- Engineering
- Closing date
- Today
- Source
- Vincere
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
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Expanding the core learning framework that governs how agents improve
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Designing structured context and memory layers
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Building reasoning loops and feedback systems
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Creating continuous learning pipelines from live operational data
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Shipping production-grade Python systems into real deployments
What you’ll bring
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Experience building non-trivial LLM systems in production
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Designed agentic workflows involving reasoning, memory, and tool use
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Strong Python engineering and systems thinking
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Clear ownership of end-to-end AI systems
The company
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Series A backed by Sequoia ($28M)
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Platform approaching one trillion tokens processed
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Major enterprise customers live
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Small, engineering-led team building the learning layer enterprise AI will depend on
Everyone will receive a response.