14 days ago
Base Salary
$320k - $405k/yr
Responsibilities
- Build and operate data pipelines that extract information from research training runs into fast, queryable storage.
- Develop APIs, libraries, and web interfaces for data management, exploration, and analysis.
- Create dataset management, data cataloging, and provenance tooling.
- Embed with research teams to understand workflows and deliver high-leverage solutions.
- Collaborate with adjacent teams to build on existing systems.
Requirements
- Significant software engineering experience, particularly with data-intensive applications or internal tooling.
- Ability and interest in working directly with technical users, gathering requirements iteratively, and shipping adopted tools.
- Strong candidates may have experience with large-scale ETL, columnar storage, query engines, high-volume time-series data, data cataloging, lineage, metadata management, ML experiment tracking, metrics platforms, or complex data visualization and full-stack web applications.
- A bachelor's degree or equivalent combination of education, training, and/or experience is required.
- A relevant field of study demonstrated through coursework, training, or professional experience is required.
- Prior ML or AI training experience is not required.
Benefits
- Annual base salary range of $320,000–$405,000 USD.
- Hybrid policy requiring staff to work from an Anthropic office at least 25% of the time, with some roles requiring more office time.
- Visa sponsorship may be available, with immigration-lawyer support.
- Competitive benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space.
- Anthropic is a public benefit corporation focused on reliable, interpretable, and steerable AI systems.
Tech Stack
Apache SparkGoogle BigQuery
Categories
BackendData Engineering
About Anthropic
We're an AI research company that builds reliable, interpretable, and steerable AI systems. Our first product is Claude, an AI assistant for tasks at any scale. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.