Arize AI
Developer Relations Education Engineer
About this role
Arize AI seeks a Developer Relations Education Engineer to create technical content and educational materials that teach AI engineers how to evaluate and observe AI applications. You'll produce blogs, videos, webinars, courses, and conference talks while building AI-assisted production systems to scale content across multiple formats and channels.
What you'll do
- Write technical blog posts and tutorials for Arize's blog and place guest content on partner publications
- Produce educational videos for YouTube and internal enablement, ranging from short explainers to detailed walkthroughs
- Plan and deliver high-volume webinars end-to-end, including topic selection, demos, and follow-up content
- Develop and deliver structured courses on internal and external learning platforms
- Speak at conferences and deliver hands-on workshops with accompanying demo applications
- Build AI-assisted production pipelines to repurpose one piece of research into blog posts, videos, webinars, and social content
What they're looking for
- Technical writing and explanation
- Video production and editing
- Public speaking and conference presentation
- Web development and demo application building
- AI/ML fundamentals and observability concepts
- Content strategy and multi-format adaptation
- AI automation and workflow optimization
- Community engagement and feedback incorporation
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Arize AI
Arize AI builds observability and evaluation solutions for production generative AI systems, helping enterprise customers monitor and secure their AI deployments. The company is hiring Forward Deployed AI Engineers to implement these solutions directly with clients, DevSecOps Engineers to secure AI infrastructure, and AI Sales Engineers to guide enterprise customers through technical evaluations and deployments.
- Website
- arize.ai
Likely interview questions
- Tell us about a complex technical concept you've taught to engineers—how did you approach explaining it, and how would you adapt that explanation for an AI agent-enabled audience?
- Describe your experience building AI automation pipelines or workflows. How would you design a system to turn one research piece into multiple content formats efficiently?