The Role

We’re hiring a Member of Technical Staff to own the design, development, and production of Frontier Data Products. You’ll build the sandboxed, reproducible environments AI agents rely on during training and evaluation, the terminals, browsers, and tool-augmented workspaces they operate inside.

This is a hands-on engineering role. You’ll write production-quality infrastructure, integrate with the broader RL tooling ecosystem, and partner closely with our data operations team to keep environments robust and observable for annotators and model agents alike. Above all, you’ll need a real grasp of how RL training loops consume environments and where they tend to break.

What You’ll Do

• 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

What We’re Looking For

• 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

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

Candidate Archetype

The ideal candidate is a strong software engineer first, with genuine curiosity and working knowledge of how modern AI systems are built and evaluated. You’ve probably built infrastructure or developer tooling at a startup or mid-stage company, and you’ve been pulled toward the AI space maybe through side projects, open-source contributions, or a prior role adjacent to an applied AI or ML team. You’re the kind of engineer who reads a benchmark paper or evaluation framework and immediately thinks about how to make the underlying system more robust, not just how to improve the model’s output.

You thrive in ambiguity. You can take a loosely defined project requirement, “build an environment that tests an agent’s ability to navigate a file system and execute multi-step bash workflows” and deliver a working, tested, documented system without needing a detailed spec. You move fast, but you care about reliability because you know systems that break silently poison the data everything downstream depends on.

Why This Role Matters

• Reinforcement learning has become the state-of-the-art approach for training agentic AI, and environment quality is one of its biggest bottlenecks. Environments that are brittle, non-deterministic, or poorly instrumented produce noisy signals that directly degrade model performance. You’ll be solving one of the highest-leverage infrastructure problems in AI today.
• You’ll work across a portfolio of projects spanning different AI labs and model capabilities with no single-product monotony. The environment types you build will evolve as RL continues to shape the frontier of agent capabilities.
• Alignerr is a small, high-impact team inside Labelbox. You’ll have startup-level ownership with growth-stage resources.