Labelbox

Forward Deployed Engineer, RL Environments

San Francisco Bay Area · Mid

Sponsorship not specified$140k-$200kDetected 106 days ago

PythonGoRustC++BashAWSGCPDockerCI/CDMachine LearningAgentic AIControlsTest AutomationResearch

About the role

You'll write production-quality infrastructure code, integrate with open-source RL tooling, and work closely with our data operations team to ensure environments are robust, observable, and ready for human annotators and model agents alike.
You won't be doing ML research, but you'll need to deeply understand how RL training loops consume environments and where the bottlenecks live.

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
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
Experience building or maintaining developer tooling, CLI tools, or infrastructure automation
Innovation at Speed: We celebrate those who take ownership, move fast, and deliver impact.
We empower people to drive results through clear ownership and metrics.
Ability to read and implement from academic papers and open-source benchmark repositories without extensive hand-holding
Familiarity with GCP or AWS infrastructure (Compute Engine, ECS/EKS, Cloud Build)

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.
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
Experience with agentic AI evaluation frameworks (SWE-bench, WebArena, OSWorld, TerminalBench, or similar)
You move fast, but you care about reliability because you know environments that break silently poison training data.

Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale

Labelbox strives to ensure pay parity across the organization and discuss compensation transparently.

Continuous Growth: Every role requires continuous learning and evolution.
Direct experience building or contributing to RL environments (Gymnasium/Gym, PettingZoo, or custom environment implementations)

Shape the Future of AI
At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises.
Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.
About Labelbox
We're the only company offering three integrated solutions for frontier
What We're Looking For

This listing is sourced directly from Labelbox's careers page and normalized into a canonical job model.