About the role
What we do
Ambral helps enterprises own the intelligence behind their most important workflows.
Every company has years of historical evidence showing how work gets done: the context people had, the decisions they made, the actions they took, and the outcomes that followed. Today, most of that history is inert. It isn’t structured in a way that companies can use to evaluate models and improve agent behavior.
Ambral turns this history into replayable environments and eval sets grounded in real workflows and observed outcomes. We use those environments to improve model performance through reinforcement learning and other post-training techniques, alongside context engineering, harness design, and agent engineering.
The result is better, more cost-efficient AI for each enterprise’s specific work, powered by open-weight models that the company owns and controls. This allows each company to retain ownership of its core workflow intelligence instead of outsourcing it to a model provider.
We graduated from YC S2025, raised millions in funding, and are already deployed within multi-billion dollar enterprises. Now we're growing the founding team.
What you’ll do
We’re building a replayable environment engine over real enterprise history.
The system reconstructs a company’s context as it existed at any past time, then exposes that state through the same tools an agent would use in production. This lets us place new policies and agent configurations inside real historical environments, observe how they reason and act, and grade their performance against real outcomes.
You’ll help build the infrastructure and work hands-on with customers to turn their real enterprise data into a scalable, continuous model-improvement system. The core problems include:
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Forward deploying with our customers to understand their tasks and data
