Forward Deployed Engineer, RL Environments

Summary

Labelbox is building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. They are seeking a Forward Deployed Engineer to design, develop, and operationalize reinforcement learning environments for AI training, ensuring robust and observable environments for model agents.

Responsibilities

• 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

Skills

• 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
• 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

Qualifications

Must Haves

• 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

Nice to Haves

• 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