Head of Research

Ambral Labs; San Francisco (CA); On-site; USD 250,000 - 380,000; Full time

Benefits offered by this job

Equity

Equinox membership

Free meals

Health insurance

Unlimited PTO

Job summary

Ambral Labs is hiring a Head of Research to lead the research agenda behind our replayable enterprise history environments. You will identify high-leverage questions, design experiments, and stay hands-on building the systems that turn findings into production.

You’ll work closely with the CTO, deploy into real workflows, and shape the research culture as we scale. Ideal candidates possess a PhD in ML, deep RL/post-training experience, and a proven ability to move from hypothesis to deployed

Qualifications

• PhD in machine learning, CS, math, or equivalent research track.
• Deep experience in reinforcement learning, LLM post-training, evals, agent environments, or related areas
• Proven track record turning ambitious, open-ended research problems into working systems
• Comfort turning fuzzy business objectives into measurable tasks and signals
• Ability to move between research questions and production implementation

Responsibilities

• Own the research agenda required to advance the replayable environment engine
• Identify high-leverage technical questions and design experiments to answer them
• Remain hands-on building systems that translate research into production
• Collaborate with CTO and production teams to deploy research into enterprise workflows
• Help establish the research culture and recruit/develop the team
• Scale research output to thousands of concurrent training/evaluation runs

Skills

Reinforcement learning

LLM post-training

Experiment design

Production deployment

Education

PhD in machine learning

Tools

Python

PyTorch

RL frameworks

Job description

What We Do

Ambral Labs helps enterprises own the intelligence behind their most important workflows.

What We Do

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-efficiency 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.

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.

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

These problems are wide open. You'll have significant ownership over both the research direction and the production systems that make it real.

You’ll work directly with the CTO, deploy into real enterprise workflows, and see your research tested against consequential problems and observable outcomes.

You'll also help establish the research culture at Ambral Labs: how we run experiments, evaluate progress, choose technical bets, and recruit and develop an exceptional research team.

Who You Are

• You have a PhD in machine learning, computer science, mathematics, or an equivalent track record of significant research experience
• You have deep experience in reinforcement learning, LLM post-training, evals, agent environments, or closely related areas
• 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.

Benefits

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