Ambral Head of Research at Ambral leads the research agenda to build a replayable environment engine over real enterprise history. They design experiments, build systems, and work on advancing post-training methods for AI agents.
Responsibilities
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.
As Head of Research, you'll own the research agenda required to make that possible. You'll identify the highest-leverage technical questions, design the experiments needed to answer them, and remain deeply hands-on in building the systems that turn those answers into production.
Some of the problems you'll work on:
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
Qualification
You'll likely thrive here ifYou've taken ambitious
Required
You'll likely thrive here if:
You've taken ambitious, open-ended research problems from hypothesis through experimentation into working systems
You’re comfortable turning fuzzy business objectives into tasks and signals that can be evaluated reliably
You can move between research questions and production implementation without treating them as separate jobs
You’re looking to do the best work of your life and build something you’ll be proud of for decades
We’re especially interested in candidates who have worked at a leading foundation model lab, top AI research organization, or high-performing AI startup.
