Member of Technical Staff (RL Research & Environments)

• As a Software Engineer on the RL Research & Environments team, you will design and operate the data, evaluation, and environment systems that improve model capabilities after pre-training
• 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 ownership of major capability areas, deeper RL systems work, or broader influence over post-training strategy as Magic scales long-context model performance and reliability
• 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

Benefits

• Retirement savings: 401K with 6% salary matching
• Healthcare: Health, dental and vision insurance for you and your dependents
• Time off: Unlimited paid time off to support balance in your work and life
• Office catering: Chef-prepared lunch and dinner
• Wellbeing: Access to mental health, financial wellbeing, and telehealth programs
• Salary and equity: Competitive salary with significant equity compensation

• Track record of owning experimental or production systems end-to-end
• Strong attention to data quality and evaluation rigor
• Comfort working at the intersection of ML, data systems, and infrastructure
• Experience building or operating large-scale data or ML systems
• Strong software engineering fundamentals
• Ability to design and interpret experiments that measure model behavior changes