What are the responsibilities and job description for the Research Engineer, Research Tools (Clio) position at Anthropic?
About the role
As a Research Engineer in Researcher Productivity, you'll design and build critical infrastructure that enables and accelerates foundational research into how our AI systems impact people and society. Your work will accelerate research and positively impact our research publications, policy campaigns, safety systems, and products. You can read about the tooling and research our team builds in these blog posts: Clio: A system for privacy-preserving insights into real-world AI use and A statistical approach to model evaluations.
Strong candidates will have a track record of technical leadership around ambiguous problems, architecting and implementing high-quality internal machine learning infrastructure and data processing pipelines, working in a fast-paced startup environment, and demonstrating an eagerness to develop their research and technical skills. The ideal candidate will enjoy a mixture of direct support in unblocking and accelerating researchers, developing new infrastructure, tools, and evaluation suites, and working cross-functionally across multiple research and product teams.
Responsibilities
- Design and implement scalable technical infrastructure that enables researchers to run experiments and evaluate AI systems efficiently
- Architect systems that can handle uncertain and changing requirements while maintaining high standards of reliability
- Lead technical design discussions to ensure our infrastructure can support both current needs and future research directions
- Interface with and improve our internal technical infrastructure and tools
- Join a rotation of engineers accelerating research by supporting researchers, answering their questions, and building tooling for their needs
You may be a good fit if you
- 5 years of relevant industry engineering or research experience
- Have experience building and maintaining production-grade internal tools or research infrastructure
- Take pride in writing clean, well-documented code in Python that others can build upon
- Are comfortable making technical decisions with incomplete information while maintaining high engineering standards
- Have experience with distributed systems and can design for scale and reliability
- Have a track record of using technical infrastructure to interface effectively with machine learning models
- Have experience deriving insights from imperfect data streams
- Enjoy accelerating research and researchers through direct support
Strong candidates may also have experience with
- Maintaining large, foundational infrastructure
- Building simple interfaces that allow non-technical collaborators to evaluate AI systems
- Working with and prioritizing requests from a wide variety of stakeholders, including research and product teams
- Scaling and optimizing the performance of tools
Representative Projects
- Design and implement scalable infrastructure for running large-scale experiments on how people interact with our AI systems
- Build robust monitoring systems that help us detect and understand potential misuse or unexpected behaviors
- Create internal tools that help researchers, policy experts, and product teams quickly analyze dynamically changing AI system characteristics
