Founding Research Engineer

About Us

Ambral helps enterprises own the intelligence behind their most important workflows. We turn years of historical enterprise data—context, decisions, actions, and outcomes—into replayable environments and evaluation sets grounded in real workflows. Using these environments, we improve model performance through reinforcement learning, post-training techniques, context engineering, harness design, and agent engineering. The result is better, more cost-efficient AI tailored to each enterprise’s specific work, powered by open-weight models that the company owns and controls. We graduated from Y Combinator Summer 2025, raised millions in funding, and are already deploying within multi-billion-dollar enterprises.

Responsibilities

• Build an environment factory that converts recorded enterprise data and task definitions into runnable environments.
• Design graders that turn ambiguous business objectives into verifiable rewards.
• Develop methods for mining useful tasks, trajectories, and evaluation cases from historical workflows.
• Create eval sets that are representative, reproducible, and resistant to overfitting.
• Find the right combinations of models, tools, context, and policies to maximize performance while reducing inference cost.
• Train and evaluate agents that operate over long horizons, incomplete information, and large tool spaces.
• Build replay and observability systems that make agent behavior explainable and measurable.
• Scale from individual environments to thousands of concurrent training and evaluation runs.

You’ll own the research and infrastructure required to turn this into a scalable model-improvement system. You’ll have significant ownership over both the research direction and the production systems, work directly with the CTO, and deploy into real enterprise workflows.

Requirements

• 4+ years of experience building production software or machine-learning systems, including at least 2 years working on reinforcement-learning environments, LLM post-training, evaluation infrastructure, agent harnesses, or closely related systems.
• Understanding of how environment design, reward design, context, tooling, and policy behavior interact.
• Ability to turn fuzzy business objectives into tasks and signals that can be evaluated reliably.
• Skill in diagnosing whether a model’s limitations come from the model itself, its context, its tools, its harness, or its training.
• Ability to move between research questions and production implementation without treating them as separate jobs.
• Strong software engineering skills and experience building systems that process large, messy datasets at scale.
• Commitment to reproducibility, observability, and understanding why a model behaves the way it does.
• Desire to do the best work of your life and build something you’ll be proud of for decades.

We care much more about what you’ve built and how you think than credentials or conventional career paths.

Benefits

• Significant equity and ownership.
• Equinox membership.
• Free meals, coffee, and snacks.
• Health insurance.
• Unlimited PTO.