Research Engineer, RL Post-Training & Environments

magic.dev; San Francisco (CA); On-site; USD 275,000 - 550,000; Full time

Benefits offered by this job

Salary range 275k-550k

Equity compensation

401(k) with 6% match

Health, dental, vision insurance

Unlimited PTO

Visa sponsorship

Relocation stipend

Small fast-paced team

Job summary

Magic seeks a Research Engineer for the RL Research & Environments team to design data, evaluation, and environment systems that enhance model capabilities after pre-training. You will own the infrastructure and experiments linking product priorities to measurable capability gains, with focus on long-context challenges and rapid RL iteration at scale.

This role can evolve into ownership of major capability areas or broader post-training strategy as Magic scales long-context model performance and

Qualifications

• 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.

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.

Skills

Software engineering

ML systems

Experiment design

Production systems

Data quality

Infrastructure

Job description

Magic seeks a Research Engineer for the RL Research & Environments team to design data, evaluation, and environment systems that enhance model capabilities after pre-training. You will own the infrastructure and experiments linking product priorities to measurable capability gains, with focus on long-context challenges and rapid RL iteration at scale.

This role can evolve into ownership of major capability areas or broader post-training strategy as Magic scales long-context model performance and