Staff ML Engineer: RL Environments & Production Systems

Ambral; New York (NY); Sur place; USD 120 000 - 180 000; Plein temps

Avantages offerts par ce poste

Significant equity and ownership

Equinox membership

Free meals, coffee, and snacks

Health insurance

Unlimited PTO

Résumé du poste

Ambral Labs is hiring to grow its founding team in New York. You will work at the center of a replayable environment engine that reconstructs enterprise worlds from the past and exposes state through production-grade tools used by agents.

You’ll collaborate across research, infrastructure, and production systems with direct interaction with the CTO to test research against real business problems. The role emphasizes turning complex objectives into measurable signals, building scalable systems

Qualifications

• 1-7 years of experience building production software or machine-learning systems.
• Bonus points for reinforcement-learning environments, LLM post-training, or evaluation infrastructure.
• You understand how environment design, reward design, context, tooling, and policy behavior interact.
• You can turn fuzzy business objectives into tasks and signals that can be evaluated reliably.
• You can diagnose whether a model’s limitations come from the model itself, its context, its tools, its harness, or its training.
• You can move between research questions and production implementation without treating them as separate jobs.
• You write strong software and can build systems that process large, messy datasets at scale.
• You care about reproducibility, observability, and understanding why a model behaves the way it does.
• You’re looking to do the best work of your life and build something you’ll be proud of for decades

Responsabilités

• Designing and implementing a replayable environment engine for enterprise workflows.
• Building an environment factory that converts historical data and task definitions into runnable environments.
• Designing graders that translate business objectives into verifiable rewards.
• Developing methods to mine useful tasks, trajectories, and evaluation cases from historical workflows.
• Creating eval sets that are representative, reproducible, and resistant to overfitting.
• Finding optimal model/tool/context/policy combinations to maximize performance while reducing cost.
• Training and evaluating agents operating over long horizons and large tool spaces.
• Building replay and observability systems to make agent behavior explainable and measurable.
• Scaling from individual environments to thousands of concurrent runs.
• Owning the research direction and production systems end-to-end.

Connaissances

Production software

Machine learning systems

Research to production

Reinforcement learning

Problem solving

Outils

Python

TensorFlow/PyTorch

Git

Linux

Description du poste

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-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 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

At the center of Ambral Labs is a replayable environment engine for the enterpirse.

The system reconstructs a company’s world as it existed at a particular moment in the past 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 work across research, infrastructure, and production systems including:

• 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

• Training and evaluating 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.

Who you are

• You have 1-7 years of experience building production software or machine-learning systems (we're hiring at multiple levels for this role).

• Bonus points for working on reinforcement-learning environments, LLM post-training, evaluation infrastructure, agent harnesses, or closely related systems

• You understand how environment design, reward design, context, tooling, and policy behavior interact

• You’re comfortable turning fuzzy business objectives into tasks and signals that can be evaluated reliably

• You can diagnose whether a model’s limitations come from the model itself, its context, its tools, its harness, or its training

• You can move between research questions and production implementation without treating them as separate jobs

• You write strong software and can build systems that process large, messy datasets at scale

• You care about reproducibility, observability, and understanding why a model behaves the way it does

• You’re looking 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