Staff Engineer: Enterprise AI Systems & Environments

Ambral; New York (NY); On-site; USD 120,000 - 190,000; Full time

Benefits offered by this job

Significant equity and ownership

Equinox membership

Free meals, coffee, and snacks

Health insurance

Unlimited PTO

Job summary

Ambral is building a replayable environment engine over real enterprise history, enabling teams to reconstruct context from past data and expose it through production tooling for policy testing and agent evaluation.

You will work hands-on with customers to turn their enterprise data into a scalable, continuous model-improvement system, addressing challenges from data ingestion to evaluation and deployment.

Qualifications

• Experience building production-grade software (internships allowed).
• Ability to design systems that process large, messy datasets at scale.
• Strong intuition for systems, abstraction, and data structures.
• Comfort turning fuzzy business objectives into actionable tasks and signals.

Responsibilities

• Build the replayable environment engine over real enterprise history.
• Collaborate with customers to turn their data into a scalable model-improvement system.
• Develop infrastructure to expose state through production-style tooling used by agents.
• Design graders to convert business objectives into verifiable rewards.
• Mine useful tasks, trajectories, and evaluation cases from historical workflows.
• Experiment with learning objectives and task design.
• Create eval sets that are representative, reproducible, and resistant to overfitting.
• Find model-tool-context-policy combinations to maximize performance while reducing inference cost.
• Build replay and observability systems to make agent behavior explainable.

Skills

Production-grade software

Large datasets

Systems design

Problem solving

Communication

Tools

Python

SQL/Databases

Job description

Ambral is building a replayable environment engine over real enterprise history, enabling teams to reconstruct context from past data and expose it through production tooling for policy testing and agent evaluation.

You will work hands-on with customers to turn their enterprise data into a scalable, continuous model-improvement system, addressing challenges from data ingestion to evaluation and deployment.