About the Role
We seek an experienced Engineer to join our Model Artifacts team empowering Research at Anthropic. This team builds libraries, tooling, and infrastructure to make Research workflows more productive, efficient, collaborative, and safe. You will design and develop scalable systems that empower Anthropic's researchers to effectively track, analyze, and leverage data throughout the ML lifecycle, from training to model evaluation. Working closely with our research organization, you'll build and maintain critical infrastructure that supports groundbreaking AI research while ensuring system reliability, performance, and usability. Your work will directly impact Anthropic's ability to advance the frontiers of AI in a safe and responsible manner.
Responsibilities
- Design, build, and improve data engineering systems for research workflows, including data tracking, caching, and analysis
- Enhance and maintain our core artifact systems and other research productivity tools
- Collaborate with researchers to understand their needs and build solutions that make their workflows more efficient and reproducible
- Develop and optimize data pipelines for collecting, processing, and analyzing research data
- Build and maintain backends, UIs, and APIs that allow researchers to explore and utilize experimental data effectively
- Improve system performance, reliability, and scalability to handle increasingly complex research needs
- Implement monitoring, testing, and documentation to ensure system reliability and ease of use
- Participate in your team's on-call rotation, deliver operationally ready code, and exercise a high degree of customer focus in your work
- Work collaboratively with other engineering teams to integrate research tools with broader company infrastructure
You May Be a Good Fit If You
- Have 5+ years of engineering experience with a strong focus on Machine Learning infrastructure or Data Engineering
- Have experience building and maintaining data pipelines and infrastructure
- Are proficient in Python and comfortable working in a Linux environment
- Have experience with distributed systems and cloud infrastructure
- Are familiar with ML workflows and the technical needs of ML research
- Can effectively communicate technical concepts to both technical and non-technical stakeholders
- Are results-oriented, with a bias towards flexibility and impact
- Pick up slack, even if it goes outside your job description
- Enjoy pair programming and debugging as a way to learn and teach
- Have a desire to make researchers more productive through both infrastructure/libraries and direct support
- Are committed to developing AI responsibly and safely
Strong Candidates May Also Have Experience With
- ML infrastructure development or ML platform engineering
- Big data technologies (e.g., BigTable, BigQuery, Spark)
- Containerization and orchestration tools (e.g., Docker, Kubernetes)
- Working directly with ML researchers or in an ML research organization
- Understanding of large language models and their training/evaluation pipelines
- Building caching systems or data versioning tools
Deadline to apply: None. Applications will be reviewed on a rolling basis.
