Member of Technical Staff

Join Labelbox, a leading AI data company, as a Member of Technical Staff. In this hands-on engineering role, you will design, develop, and maintain sandboxed reinforcement learning environments for agentic AI training. You will work closely with our data operations team to ensure the robustness and observability of these environments. This position offers a flexible vacation policy, 401k program, college savings account, HSA, daily lunches, virtual wellness programs, a dog-friendly office, regular company social events, professional development benefits, and remote work options.

• Conception, développement et production de produits de données Frontier.
• Création d'environnements sandboxés et reproductibles pour la formation et l'évaluation des agents d'IA.
• Intégration et extension des outils d'agentic open-source et des interfaces CLI/API personnalisées.

• Strong debugging instincts, you can trace failures across process boundaries, container layers, and network calls
• Experience building or maintaining developer tooling, CLI tools, or infrastructure automation
• 2+ years of professional software engineering experience, with strong fundamentals in Python and at least one systems-level language (Go, Rust, C++)
• Ability to read and implement from academic papers and open-source benchmark repositories without extensive hand-holding
• Demonstrated experience with containerization and sandboxing (Docker, Podman, Firecracker, or similar) in production or near-production contexts
• Comfort working with browser automation frameworks or terminal interaction tooling
• 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
• 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’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
• 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
• 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