Member of Technical Staff, RL Research & Environments
About the role
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 pre-training, domain-specific RL, ultra-long context, and inference-time compute to achieve this goal.
Responsibilities
- Design and build post-training datasets using synthetic generation, targeted data collection, and self-play
- Implement filtering, scoring, and mixture strategies for RL and post-training corpora
- Build and maintain evaluation frameworks that surface long-context failure modes
- Design reward signals and training environments for targeted capability improvements
- Run ablations across data sources, reward designs, and long-horizon task structures
- Improve reliability and observability of post-training data and environment pipelines
- Collaborate closely with Product and Research to translate capability goals into measurable iteration cycles
Requirements
Strong software engineering fundamentals
Experience building or operating large-scale data or ML systems
Ability to design and interpret experiments that measure model behavior changes
Comfort working at the intersection of ML, data systems, and infrastructure
Strong attention to data quality and evaluation rigor
Track record of owning experimental or production systems end-to-end
Qualifications
Not specified
Skills
Not specified
Benefits
Not specified
Pay
Annual salary range: $200K - $550K based on experience
Schedule
Not specified