Staff ML Engineer, Agent Training & Environments

Job summary

Responsibilities

Labelbox is the RL data factory for advancing frontier agent capabilities. We build the data, environments, and evaluations that frontier labs use to train and judge their agents.

• RL environments for agentic tasks: task definitions, tool surfaces, state and reset semantics, reward design — and the harness that runs thousands of them in parallel.
• Verifiers and graders: programmatic checks, LLM judges, rubric pipelines, pass@k scoring. Deciding what "the agent succeeded" means, and making that judgment trustworthy at scale.
• Fine-tuning pipelines that turn evaluation signals into measurable agent improvements — SFT and RL, from data collection through training to checkpoint evaluation.
• Eval systems that run millions of agent trajectories to measure model and product quality.
• Training and serving infrastructure that scales to the throughput frontier labs need: multi-launcher orchestration, long-running job fault tolerance, cost accounting.

Tech stack

• Python
• React
• TypeScript
• Node.js
• Java
• GCP
• Kubernetes
• PostgreSQL