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Member of Technical Staff

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Member of Technical Staff in USA new

  • Labelbox
  • Full time
  • Email
  • San Francisco, CA

Responsibilities

  • Design, build, and maintain sandboxed reinforcement-learning environments for agentic AI training, including terminal emulators, browser automation harnesses, computer-use simulators, and tool-augmented workspaces.
  • Develop reproducible, containerized execution environments using Docker, virtual machines, and lightweight sandboxes for deterministic task rollouts and reward collection.
  • Integrate and extend open-source agentic tooling, custom CLI/API harnesses, and browser or terminal interaction systems.
  • Build observability and instrumentation for structured logging, trajectory capture, state snapshotting, and auditable training and annotation data.
  • Partner with data operations on task curricula and evaluation protocols across environment types.
  • Own environment deployment and reliability through CI/CD pipelines, automated configuration testing, and monitoring for drift or breakage.
  • Rapidly prototype and deliver new environment types from loosely defined requirements.

Requirements

  • At least 2 years of professional software engineering experience.
  • Strong Python fundamentals and proficiency in at least one systems-level language: Go, Rust, or C++.
  • Production or near-production experience with containerization and sandboxing such as Docker, Podman, or Firecracker.
  • Working knowledge of reinforcement-learning concepts including MDPs, reward shaping, episode structure, and observation/action spaces.
  • Experience building or maintaining developer tooling, CLI tools, or infrastructure automation.
  • Familiarity with browser automation frameworks or terminal interaction tooling.
  • Strong debugging ability across process boundaries, container layers, and network calls.
  • Ability to implement concepts from academic papers and open-source benchmark repositories independently.
  • Preferred: direct experience with RL environments such as Gymnasium/Gym, PettingZoo, or custom implementations.
  • Preferred: experience with agentic-AI evaluation frameworks such as SWE-bench, WebArena, OSWorld, or TerminalBench.
  • Preferred: familiarity with GCP or AWS infrastructure, including Compute Engine, ECS/EKS, or Cloud Build.
  • Preferred: prior experience at an AI data company, ML platform company, or AI research lab, or contributions to relevant open-source projects.

Benefits

  • Annual base salary range of $140,000—$200,000 USD, excluding equity and additional benefits.
  • Startup-level ownership and growth-stage resources within a small, high-impact team.
  • Career advancement tied to impact and opportunities to work on frontier AI development.
  • Fast-paced, high-intensity environment focused on ownership, autonomy, and rapid execution.

Salary: $140k - $200k/yr

Labelbox

Labelbox builds a data platform and managed services for creating, managing, and evaluating training data for AI models, including computer vision, NLP, and RLHF workflows. The company sells subscriptions and labeling services to enterprises and AI labs; founded in 2018 and headquartered in San F...

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