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AmbralAmbralNew York, NY

Founding Research Engineer

Build research and production infrastructure for replayable enterprise environments, agent evaluation, and model improvement. The role requires 4+ years building production software or ML systems, including 2+ years in reinforcement-learning environments, LLM post-training, evaluation infrastructure, agent harnesses, or related work.

215k – 330k/yr
On-site4+ YOEML Engineering

About the role

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 combinations of models, tools, context, and policies that maximize performance while reducing inference cost.
  • Train and evaluate agents operating 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.
  • Own the research and infrastructure needed to create a scalable model-improvement system.
  • Deploy research into real enterprise workflows and work directly with the CTO.

Requirements

  • 4+ years of experience building production software or machine-learning systems.
  • 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 ambiguous business objectives into reliably evaluable tasks and signals.
  • Ability to diagnose whether model limitations originate in the model, context, tools, harness, or training.
  • Ability to move between research questions and production implementation.
  • Strong software engineering skills and experience building systems that process large, messy datasets at scale.
  • Care for reproducibility, observability, and understanding model behavior.
  • Credentials are not required; demonstrated work and problem-solving ability are valued.

Benefits

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

Skills

Reinforcement Learningllm post-trainingMachine Learningevaluation infrastructureagent harnessesPythonlarge-scale data processingreward designenvironment designcontext engineeringObservabilityModel Training

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