Magic
Member of Technical Staff, RL Research & Environments
San Francisco · Staff+
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odds of building a lasting career here
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →H-1B wage level
the lottery is wage-weighted — each level is one more entry
This range already reaches Level IV — the maximum four lottery entries.
DOL prevailing wage, 2026-27 wage year · Software Developers (15-1252) · San Francisco-Oakland-Fremont, CA. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
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About the role
- 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.
- 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.
Responsibilities
- 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 pre-training.
- You will own the infrastructure and experimental workflows that connect product priorities to concrete capability gains.
- You will build systems that expose failure modes, generate high-signal training data, and enable rapid RL iteration at scale.
- 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
- Design reward signals and training environments for targeted capability improvements
- Collaborate closely with Product and Research to translate capability goals into measurable iteration cycles
Requirements
- Comfort working at the intersection of ML, data systems, and infrastructure
- Track record of owning experimental or production systems end-to-end
Skills
- Improve reliability and observability of post-training data and environment pipelines
Compensation
- Equity is a significant part of total compensation, in addition to salary
- 401(k) plan with 6% salary matching
- Annual salary range:s between $275K - $550K based on experience
Benefits
- Equity is a significant part of total compensation, in addition to salary
- 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
Company info
- Magic's mission is to build safe AGI that accelerates humanity's progress on the world's most important problems.
Visa & Work Authorization
- Visa sponsorship and relocation stipend to bring you to SF, if possible
This listing is sourced directly from Magic's careers page and normalized into a canonical job model.