Member of Technical Staff
Hybrid (San Francisco Bay Area)$140k - $200kFull-timePosted 4w ago
About the role
Hands-on engineering role owning design, development, and production of Frontier Data Products. Build sandboxed, reproducible environments for RL training and evaluation, terminals, browsers, and tool-augmented workspaces. Collaborate with data operations to ensure robust, observable environments and integrate with RL tooling ecosystem.
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
Qualifications
- 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 instincts, able 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
Skills
Required:PythonSystems programming (Go, Rust, or C++)Containerization and sandboxingRL conceptsDeveloper tooling / infrastructure automationBrowser automation / terminal tooling
Last updated Sep 23, 2026. Interested? Apply on the company site.
About Labelbox
The data factory for AI teams
Labelbox delivers the software and services to help you build, operate, or staff your data factory
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