Forward Deployed Engineer, RL Environments
Hybrid (San Francisco Bay Area)$140k - $200kfull-timePosted 6mo ago
About the role
Role focused on design, development, and operationalization of reinforcement learning environments and sandboxed execution environments for agentic AI training and evaluation.
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, 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
Skills
Required:PythonSystems-level programmingContainerization/sandboxingRL conceptsDeveloper tooling / CLIBrowser automation / terminal tooling
Benefits
- Note: Benefits are not explicitly itemized in the provided description; skipped.
Last updated Sep 2, 2026. Interested? Apply on the company site.
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