Forward Deployed Engineer, RL Environments

About the role

The Forward Deployed Engineer will design, develop, and operationalize reinforcement learning environments. They will build sandboxed, reproducible execution environments for agentic AI training, ensuring robustness, observability, and usability for both human annotators and 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.
• 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, such as structured logging, trajectory capture, and state snapshotting, to ensure 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, including 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.

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
• 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

Benefits

High-Impact Environment: Operate like an early-stage startup, focusing on impact over process. Rapid career growth tied to contributions.

Technical Excellence: Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence.

Innovation at Speed: Celebrate ownership, move fast, and deliver impact. Reward high agency and rapid execution.

Continuous Growth: Every role requires continuous learning and evolution. Surrounded by curious minds solving complex problems at the frontier of AI.

Clear Ownership: Know exactly what you're responsible for and have the autonomy to execute. Empowerment through clear ownership and metrics.

Pay

Annual base salary range: $140,000 - $200,000 USD

Schedule

Hybrid model with 3 days per week in office, combining collaboration and flexibility.