# Member of Technical Staff

**Company:** [Ambral](https://hotfix.jobs/companies/ambral)
**Location:** New York, NY, San Francisco, CA
**Role:** ML Engineering
**Salary:** $140k – $175k/yr
**Experience:** 0+ years
**Skills:** Python, Reinforcement Learning, Post-Training, Context Engineering, Agent Engineering, Data Structures, Large-Scale Data Processing, Model Evaluation, Observability, Data Mining
**Posted:** 2026-08-17

> Build production infrastructure for replayable enterprise environments, agent evaluation, and continuous model improvement. The role combines hands-on customer deployment, research experimentation, large-scale data processing, and production software engineering.

## Job Description

## Responsibilities
- Forward deploy with customers to understand their tasks and data.
- Develop an environment factory that converts recorded enterprise data and task definitions into runnable environments.
- Design graders that turn ambiguous business objectives into verifiable rewards.
- Mine useful tasks, trajectories, and evaluation cases from historical workflows.
- Experiment with learning objectives and task design.
- Create representative, reproducible eval sets resistant to overfitting.
- Find effective combinations of models, tools, context, and policies while reducing inference cost.
- Build replay and observability systems that make agent behavior explainable and measurable.
- Own production systems, deploy into enterprise workflows, and work directly with the CTO and customers.

## Requirements
- Experience building production-grade software; internships are acceptable, or experience in a quant role.
- Strong software development skills and ability to build systems that process large, messy datasets at scale.
- Strong intuition for systems, abstraction, and data structures.
- Ability to turn fuzzy business objectives into reliably evaluable tasks and signals.
- Ability to diagnose whether model limitations arise from the model, context, tools, harness, or training.
- Ability to move between research questions and production implementation.
- Care for reproducibility, observability, and understanding model behavior.

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

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