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 builds frontier-scale code models designed to act as an AI coworker for software developers and engineering teams, automating code generation and research tasks. Its products center on developer-facing models and tooling that integrate into software workflows for teams seeking higher velocity and reliability. Founded in 2022 and headquartered in San Francisco, the privately held company focuses on AI-driven developer tools spanning information technology and machine learning.