OpenAI
Analytics Engineer, Safety Systems
About this role
Build data infrastructure and dashboards to support AI safety decision-making at OpenAI. As an Analytics Engineer on the Safety Systems team, you'll create canonical data sources and intuitive analytics products that help researchers, engineers, and leadership understand and act on safety metrics for responsible AI deployment.
What you'll do
- Design and maintain canonical datasets serving as authoritative sources for safety metrics
- Develop dashboards, reports, and data products enabling stakeholders to independently analyze safety information
- Partner with Engineering, Research, and Data Science teams to understand analytical needs and design intuitive consumption interfaces
- Ensure accuracy, reliability, and user-friendliness across all analytics and visualizations
- Advocate for data quality, consistency, and reliability standards
- Build agentic workflows and machine-readable interfaces for safety data access
What they're looking for
- SQL and large dataset extraction
- ETL workflow design
- Business intelligence tools (Tableau, Looker)
- Communication and cross-functional collaboration
- Attention to detail and accuracy
- LLM-powered or agentic data tools (preferred)
- Advanced visualization libraries like Streamlit or Plotly Dash (preferred)
- NLP or large language model familiarity (preferred)
Benefits
- Hybrid work model (3 days/week in office)
- Relocation assistance available
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
OpenAI
OpenAI builds AI infrastructure and products, including large-scale data center campuses for AI computing and generative AI applications for enterprise customers. The company is hiring civil engineers, project engineers, electrical design engineers, data center R&D engineers, and AI deployment engineers to expand its infrastructure capabilities and help customers deploy AI solutions.
View all jobs at OpenAILikely interview questions
- Walk us through a time you owned an ambiguous, high-impact analytics problem from start to finish—how did you structure the approach and drive recommendations?
- Describe your experience designing and maintaining canonical datasets or sources of truth. What challenges did you face around data quality and consistency?