Software Engineer - RL Environments

As a SWE (Environments), you will design the datasets and evaluation rubrics that directly influence how frontier models learn. You'll work hands-on with research teams at top AI labs, experimenting with data-collection strategies, diagnosing model failure modes, and developing metrics to determine whether a model is actually improving. You'll go from hypothesis to live experiment quickly, and your output will feed directly into model training runs at scale.

Day-to-day, you will design data slices that expose meaningful failure modes across domains such as finance, code, and enterprise workflows. You will build and refine reward signals for RLHF and RLVR pipelines. You will develop quantitative frameworks for measuring dataset quality, diversity, and downstream impact on alignment and capability. You will partner with lab research teams to translate their training objectives into concrete data and evaluation specifications.

What You'll Do

Design data slices and explore data shapes that expose meaningful model failure modes across domains like finance, code, and enterprise workflows

Build and refine evaluation rubrics and reward signals for RLHF and RLVR training pipelines

Model annotator behavior and run experiments to improve different model capabilities

Develop quantitative frameworks for measuring dataset quality, diversity, and downstream impact on model alignment and capability

Create and manage both real-world and synthetic data pipelines

Partner with lab research teams to translate their training objectives into concrete data and evaluation specifications