almost 2 years ago
Base Salary
$200k - $550k/yr
Responsibilities
- Design and build post-training datasets using synthetic generation, targeted data collection, and self-play.
- Implement filtering, scoring, and mixture strategies for reinforcement-learning 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 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 machine learning systems.
- Ability to design and interpret experiments measuring changes in model behavior.
- Comfort working across machine learning, data systems, and infrastructure.
- Strong attention to data quality and evaluation rigor.
- Track record of owning experimental or production systems end-to-end.
Benefits
- Equity in addition to salary
- 401(k) plan with 6% salary matching
- Health, dental, and vision insurance for employees and dependents
- Unlimited paid time off
- Visa sponsorship and relocation stipend to San Francisco, if possible
- Small, fast-paced, highly focused team
Categories
AI ResearchML Engineering
About Magic
Magic is working on frontier-scale code models to build a coworker, not just a copilot. Come join us: http://magic.dev