Member of Technical Staff

Labelbox•San Francisco, CA

18h•$140,000 - $200,000

About The Position

At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially. We're the only company offering three integrated solutions for frontier AI development: Enterprise Platform & Tools, Frontier Data Labeling Service, and Expert Marketplace. We operate like an early-stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions. Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence. We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution. Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI. You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics.

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. 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

Nice To Haves

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

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