6 months ago
Base Salary
$200k - $550k/yr
Responsibilities
- Lead tooling efforts across the stack, including build systems, continuous integration, CLI tools, and internal web UIs.
- Build tools for dataset exploration, data labeling, data-quality review, and data-inventory tracking.
- Improve data-infrastructure ergonomics, including I/O patterns in Ray/dataflow jobs, dataset tracking, and pipeline observability.
- Engage with the team to identify pain points and proactively improve workflows.
- Improve code organization, packaging, and engineering best practices.
- Own how the pre-training data team works day to day and ship rapid tooling improvements.
Requirements
- Strong software engineering fundamentals.
- Genuine interest in developer experience, code organization, and engineering best practices.
- Strong communication and ability to engage teammates to understand their needs.
- Bias toward action and willingness to fix broken workflows proactively.
- Open-source contribution experience, especially with developer-facing tools such as Ruff, uv, or similar projects, is preferred.
- Build systems, continuous integration, or developer-tooling experience at scale is preferred.
- Startup product-development experience and comfort rapidly prototyping and iterating are preferred.
- Deep ML/AI expertise and prior experience specifically in data-engineering pipelines are not required.
- Based in San Francisco and able to work in the office.
Benefits
- Annual salary range of $200K-$550K based on experience, plus significant equity.
- 401(k) plan with 6% salary matching.
- Health, dental, and vision insurance for the employee and dependents.
- Unlimited paid time off.
- Visa sponsorship and relocation stipend to San Francisco, if possible.
- In-office role based in San Francisco.
- Small, fast-paced, highly focused team.
Categories
Data EngineeringDevOps
About Magic
Magic is working on frontier-scale code models to build a coworker, not just a copilot. Come join us: http://magic.dev
