San Francisco, CA
Data factory for AI teams
Labelbox provides unified software and expert labeling services to create high-quality training data for AI models. They serve leading AI labs and enterprises building generative AI, robotics, and multimodal systems. It enables faster innovation by powering breakthroughs in AI research and real-world applications.
Build RL environments, verifiers, fine-tuning pipelines, and eval systems for frontier AI agents at Labelbox. Requires deep RL post-training experience (SFT + RL methods), strong Python/systems engineering, and the ability to ship production infrastructure at high velocity.
Build and scale eval systems, fine-tuning pipelines, agent-first infrastructure, and data platforms that power frontier AI labs at Labelbox. Requires full-stack prototyping expertise, strong architecture judgment, daily use of coding agents, and deep TypeScript/Python proficiency (7+ years implied by Staff level).
Security Engineering Intern at Labelbox supporting vulnerability management, IAM workflows, cloud access controls, SIEM optimization, and CI/CD security. Requires current pursuit of a CS/Cybersecurity degree, interest in cloud/data security, and basic cloud platform knowledge.
Owns end-to-end delivery and economics for complex customer deployments. Coordinates TPMs and FDEs, manages margin, and serves as primary customer contact for timelines, quality, and operational performance.
Lead and grow the Forward Deployed Engineering team, setting technical craft standards for data pipelines, scoping, and quality evaluation while coaching FDEs and managing staffing across customer projects.
Forward Deployed Research Scientist collaborates with frontier AI labs on data strategies, fine-tunes open-weight LLMs, runs ablation studies, and validates data impact for client projects. Requires MS/PhD in ML/NLP/CS, hands-on LLM fine-tuning, and fast-paced experimental rigor.
Builds and maintains sandboxed, reproducible RL environments for AI agent training, including terminal, browser, and tool-augmented setups. Requires 2+ years Python/systems engineering, containerization, and RL concepts understanding.