Research Engineer; RL Environment

Overview

Agents do not improve in a vacuum. They need environments to operate in, tasks to solve, and clear signals for what good looks like. This role exists to build that layer.

This is a VC-backed challenger lab building state-of-the-art computer‑use agents. Recent progress has made performance highly competitive on computer‑use style benchmarks, and the company has launched a more visible product layer to make the technology easier to demonstrate.

The team you'd be joining builds the playground itself: synthetic websites, structured workflows, task sets, and evaluation environments where agents can act, fail, retry, and learn.

• Build training and evaluation environments for agentic systems.
• Create synthetic websites, workflows, and task suites that reflect useful real‑world work.
• Define reward signals and success criteria for agent behavior in structured environments.
• Turn documentation, tools, and existing workflows into interactive agent tasks.
• Improve the realism, coverage, and difficulty of training environments over time.
• Partner with research teams to convert product failures into better environments and tasks.
• Build internal tooling to generate, run, and measure large task sets.

• Strong software engineering skills, ideally in Python plus web or backend systems.
• Experience with RL, reward design, or synthetic data generation.
• Experience building internal tools, simulations, evaluation systems, or synthetic environments.
• Ability to structure ambiguous workflows into clear tasks with measurable outcomes.
• Good product instinct for what makes an environment realistic and useful for agents.
• Comfortable working at the intersection of engineering, research, and experimentation.
• High ownership and a practical mindset.