Head of Research

Ambral•New York City, NY

About The Position

Ambral Labs 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 can own and control rather than permanently renting from a model provider. We're YC S2025 , have raised millions in funding, and are already deployed inside multi-billion dollar enterprises. Now we're growing the founding team.

Requirements

PhD in machine learning, computer science, mathematics, or an equivalent track record of significant research experience.
Deep experience in reinforcement learning, LLM post-training, evals, agent environments, or closely related areas.
Have taken ambitious, open-ended research problems from hypothesis through experimentation into working systems.
Comfortable turning fuzzy business objectives into tasks and signals that can be evaluated reliably.
Can move between research questions and production implementation without treating them as separate jobs.
Looking to do the best work of your life and build something you’ll be proud of for decades.
Worked at a leading foundation model lab, top AI research organization, or high-performing AI startup.

Responsibilities

Own the research agenda required to make replayable environments over real enterprise history possible.
Identify the highest-leverage technical questions.
Design the experiments needed to answer them.
Remain deeply hands-on in building the systems that turn those answers into production.
Building an environment factory that converts recorded enterprise data and task definitions into runnable environments.
Designing graders that turn ambiguous business objectives into verifiable rewards.
Developing methods for mining useful tasks, trajectories, and evaluation cases from historical workflows.
Creating eval sets that are representative, reproducible, and resistant to overfitting.
Finding the right combinations of models, tools, context, and policies to maximize performance while reducing inference cost.
Advancing post-training methods for agents that operate over long horizons, incomplete information, and large tool spaces.
Building replay and observability systems that make agent behavior explainable and measurable.
Scaling from individual environments to thousands of concurrent training and evaluation runs.
Establish the research culture at Ambral Labs: how we run experiments, evaluate progress, choose technical bets, and recruit and develop an exceptional research team.