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 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.

What you’ll work on

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

What we’re looking for

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

Our culture

Integrity. Words and actions should be aligned

Hands-on. At Magic, everyone is building

Teamwork. We move as one team, not N individuals

Focus. Safely deploy AGI. Everything else is noise

Quality. Magic should feel like magic

Magic strives to be the place where high-potential individuals can do their best work. We value quick learning and grit just as much as skill and experience.

Compensation, benefits, and perks (US)

Annual salary range:s between $275K - $550K based on experience

Equity is a significant part of total compensation, in addition to salary

401(k) plan with 6% salary matching

Generous health, dental and vision insurance for you and your dependents

Unlimited paid time off

Visa sponsorship and relocation stipend to bring you to SF, if possible

A small, fast-paced, highly focused team