Member of Technical Staff, RL Research & Environments

Magic’s mission is to build safe AGI that accelerates humanity’s progress on the world’s most important problems. We believe the most promising path to safe AGI lies in automating research and code generation to improve models and solve alignment more reliably than humans can alone. Our approach combines frontier-scale pretraining, domain-specific RL, ultra-long context, and inference-time compute to achieve this goal.

About the role

As a Research Engineer on the RL Research & Environments team, you will design and operate the data, evaluation, and environment systems that improve model capabilities after pretraining.

This role focuses on post-training: identifying capability gaps, building targeted datasets, designing reward signals, and running iterative training loops that measurably improve user-facing behavior. You will own the infrastructure and experimental workflows that connect product priorities to concrete capability gains.

Magic’s long-context models introduce distinct post-training challenges: long-horizon reasoning, sustained coherence over extended trajectories, context-use quality, and tool-augmented behavior. You will build systems that expose failure modes, generate high-signal training data, and enable rapid RL iteration at scale.

This role can evolve into ownershi