# Forward Deployed Engineer, RL Environments

**Company:** [Labelbox](https://hotfix.jobs/companies/labelbox)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $140k – $200k/yr
**Experience:** 2+ years
**Skills:** Python, Docker, Go, Rust, C++, Reinforcement Learning, Gymnasium, Pettingzoo, GCP, AWS
**Posted:** 2026-04-07

> Builds and maintains sandboxed, reproducible RL environments for AI agent training, including terminal, browser, and tool-augmented setups. Requires 2+ years Python/systems engineering, containerization, and RL concepts understanding.

## Job Description

## What You’ll Do

- Design, build, and maintain sandboxed RL environments for agentic AI training—including terminal emulators, browser automation harnesses, computer-use simulators, and tool-augmented workspaces (e.g., environments built on frameworks like TerminalBench, OSWorld, and Tau-bench)
- Develop reproducible, containerized execution environments (Docker, VMs, lightweight sandboxes) that support deterministic task rollouts and reward signal collection
- Integrate with and extend open-source agentic tooling and custom CLI/API harnesses to enable multi-step agent interaction
- Build instrumentation and observability layers—structured logging, trajectory capture, state snapshotting—so training runs and human annotation sessions produce clean, auditable data
- Collaborate with data operations to design task curricula and evaluation protocols that stress-test model capabilities across environment types
- Own environment deployment and reliability: CI/CD pipelines, automated testing of environment configurations, and monitoring for drift or breakage across versions
- Rapidly prototype new environment types as client and internal requirements evolve, moving from spec to working system in days, not weeks

## What We’re Looking For

### Required

- 2+ years of professional software engineering experience, with strong fundamentals in **Python** and at least one systems-level language (**Go**, **Rust**, **C++**)
- Demonstrated experience with containerization and sandboxing (**Docker**, **Podman**, **Firecracker**, or similar) in production or near-production contexts
- Familiarity with RL concepts: MDPs, reward shaping, episode structure, observation/action spaces. You don’t need to have trained models, but you need to understand what an environment must provide to an RL training loop
- Experience building or maintaining developer tooling, CLI tools, or infrastructure automation
- Comfort working with browser automation frameworks or terminal interaction tooling
- Strong debugging instincts—you can trace failures across process boundaries, container layers, and network calls
- Ability to read and implement from academic papers and open-source benchmark repositories without extensive hand-holding

### Preferred

- Direct experience building or contributing to RL environments (**Gymnasium/Gym**, **PettingZoo**, or custom environment implementations)
- Experience with agentic AI evaluation frameworks (**SWE-bench**, **WebArena**, **OSWorld**, **TerminalBench**, or similar)
- Familiarity with **GCP** or **AWS** infrastructure (Compute Engine, ECS/EKS, Cloud Build)
- Prior work at an AI data company, ML platform company, or AI research lab
- Contributions to open-source projects in the RL, agents, or dev-tools space

## Compensation

Annual base salary range **$140,000—$200,000 USD**

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