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Salary: USD 140,000 - 200,000 / annual
Labelbox is hiring a Member of Technical Staff to own the design, development, and production of Frontier Data Products—specifically the sandboxed, reproducible environments that AI agents rely on during training and evaluation. You'll build and maintain terminal emulators, browser automation harnesses, computer-use simulators, and tool-augmented workspaces that enable reinforcement learning training loops.
This is a hands-on engineering role focused on production-quality infrastructure. You'll design containerized execution environments (Docker, VMs, lightweight sandboxes) that support deterministic task rollouts and reward signal collection. You'll integrate with open-source agentic tooling and custom CLI/API harnesses to enable multi-step agent interaction, and build instrumentation and observability layers—structured logging, trajectory capture, state snapshotting—so training runs and human annotation sessions produce clean, auditable data.
You'll collaborate with the data operations team to design task curricula and evaluation protocols that stress-test model capabilities across environment types. You'll own environment deployment and reliability, including CI/CD pipelines, automated testing of environment configurations, and monitoring for drift or breakage across versions. You'll rapidly prototype new environment types as client and internal requirements evolve, moving from spec to working system in days, not weeks.
Labelbox operates like an early-stage startup with high-impact focus. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions. The company is backed by SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins, and serves Fortune 500 enterprises and leading AI labs.
REQUIREMENTS:
- 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; ability to 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
