Long-Horizon Coding Task Expert

Build long-horizon RL environments and tasks for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts

Shape environments end to end: stateful, resumable systems with snapshotting, checkpointing, and branching rollouts, at multi-node scale where needed

Generate and refine ideas for tasks and agentic trajectories across multi-turn, tool-using, and computer-use agent loops with persistent state across turns

Design reward structure for sparse-reward settings: milestone and process rewards, subgoal and task decomposition, and credit assignment across long trajectories

Validate that the work is correct, hard, and covers the right edge cases, including rubric design for partial credit, contamination avoidance, and defenses against reward hacking

Design curricula that ramp task difficulty rather than shipping fixed-difficulty tasks

Build automated pipelines that curate high-quality long-horizon RL environments and tasks at scale

Write reliable, well-tested Python infrastructure rather than one-off research scripts

Work directly with our research team with high ownership, building novel systems rather than maintaining legacy ones