Forward Deployed Engineer, RL Environments
$140k - $200k • Remote • San Francisco Bay Area
Posted 1mo ago
Job Location
San Francisco Bay Area
Tech Stack
Remote Work Policy
Fully remote
Categories
LLM Engineer
About the job
Labelbox is seeking a Forward Deployed Engineer to design, develop, and operationalize reinforcement learning (RL) environments. This role involves building sandboxed, reproducible execution environments for AI agents during training and evaluation, such as terminal-based task benchmarks, browser environments, and tool-augmented workspaces. You will write production-quality infrastructure code, integrate with open-source RL tooling, and collaborate with the data operations team to ensure environments are robust and observable. While not a research role, a deep understanding of how RL training loops consume environments and identify bottlenecks is essential.
Responsibilities
- Design, build, and maintain sandboxed RL environments for agentic AI training, including terminal emulators, browser automation harnesses, and tool-augmented workspaces.
- Develop reproducible, containerized execution environments (Docker, VMs, lightweight sandboxes) for deterministic task rollouts and reward signal collection.
- Integrate with and extend open-source agentic tooling and custom CLI/API harnesses for multi-step agent interaction.
- Build instrumentation and observability layers (structured logging, trajectory capture, state snapshotting) for clean, auditable data from training runs and human annotation sessions.
- Collaborate with data operations to design task curricula and evaluation protocols that stress-test model capabilities.
- Own environment deployment and reliability, including CI/CD pipelines, automated testing, and monitoring for drift.
- Rapidly prototype new environment types based on evolving client and internal requirements.
Requirements
- 2+ years of professional software engineering experience.
- 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).
- Experience building or maintaining developer tooling, CLI tools, or infrastructure automation.
- Comfort working with browser automation frameworks or terminal interaction tooling.
- Strong debugging skills across process boundaries, container layers, and network calls.
- Ability to read and implement from academic papers and open-source benchmark repositories.