Anthropic is seeking a Staff+ Research Engineer to build and operate the RL Data Platform’s data collection interfaces, backend services, and pipelines. The role involves designing reliable, user‑friendly tooling for researchers and annotators, collaborating closely with RL researchers to translate data needs into production‑ready solutions, and owning end‑to‑end projects from concept to deployment.
Staff+ Research Engineer, RL Data Platform en Anthropic
Híbrido - San Francisco, CA, United States; New York City, NY, United States
Más vacantes en AnthropicSalary
USD 500,000 - 850,000
Requirements
Skills
- Strong full-stack engineering skills with production experience in TypeScript/React on the frontend and Python on the backend
- Experience designing and operating backend services and data pipelines that other teams depend on
- Track record of owning projects end-to-end, from ambiguous brief to production
- Comfort working directly with technical stakeholders whose needs change week to week
- Effective use of AI tools in day‑to‑day work
- Care about the societal impacts of your work
- Experience building annotation, labeling, evaluation, or other human-in-the-loop data tooling
- Experience with RLHF, preference data, or other human‑feedback pipelines for ML systems
- Experience shipping researcher‑facing or other expert‑facing internal tools
- Experience running experiments on data collection interfaces and using results to improve data quality
- Experience working with crowdworker or expert vendor platforms at scale
- Familiarity with how LLMs are trained and evaluated
Responsibilities
- Design, build, and operate the feedback and data collection interfaces used by human annotators, domain experts, and internal researchers
- Build and maintain backend services, APIs, and pipelines that route model samples to humans and return structured feedback to training
- Own the reliability, latency, and usability of systems that run continuously against live model endpoints
- Partner with RL researchers to translate loosely specified data needs into well‑scoped collection campaigns and tooling
- Build dashboards, monitoring, and inspection tools so researchers can see data quality and throughput without asking an engineer
- Identify and remove bottlenecks between data requests and training mix integration
Technologies
TypeScriptReactPython
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