Member of Technical Staff at Labelbox · LumiJobs

Qualification

Familiarity with RL conceptsStrong debugging instinctsPrior work at an AI data companyCandidate ArchetypeYou thrive in ambiguity

Required

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.

Member of Technical Staff

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Labelbox

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