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Arize AI

AI model observability and evaluation platform

Applied AI Engineer

Developer Relations & AdvocacyConfirmed live in the last 24 hours
Full-timeRemote in USA

About the Job

Requirements

  • Working knowledge of generative artificial intelligence.
  • Technical experience with the large language model application development stack, such as agents, orchestration frameworks, vector databases, and evaluation libraries, with proficiency in Python.
  • Engagement in technical communities through platforms such as GitHub, Medium, DZone, Stack Overflow, Reddit, and LinkedIn.
  • Ability to devise and deliver novel activities that earn the attention and trust of artificial intelligence and machine learning practitioners.
  • Ability to learn independently, engage with technical complexity, and develop an informed point of view.
  • Excellent written and verbal communication.
  • Willingness to attend evening meetups and events in the San Francisco Bay Area.

Responsibilities

  • Write code and build examples of agents and assistants in different frameworks for the community to reference.
  • Create educational technical content for artificial intelligence developers, including GitHub repositories, blogs, eBooks, product documentation, workshops, and demos.
  • Build Claude Code and Cursor skills to automate systems.
  • Improve the growth marketing experience.
  • Represent the voice of the artificial intelligence engineer across Arize's users and the broader artificial intelligence developer community.
  • Advocate for Arize's core software-as-a-service or open-source offerings, and distill market insights and developer feedback to inform roadmap and go-to-market activities.
  • Develop best practices and novel perspectives on troubleshooting and improving production machine learning models and large language model-powered applications.
  • Plan and speak at Arize-hosted and industry events and conferences.

About the Company

Arize AI

AI model observability and evaluation platform

Arize AI provides a platform for AI observability and LLM evaluation that helps top AI teams monitor and improve models across NLP, computer vision, and recommender systems. It works by offering analytics, LLM traces, spans, RAG analysis, and task-based evaluations to troubleshoot issues and measure performance. It differentiates itself by delivering end-to-end observability and evaluation in a single platform with embedding visualization for RAG to pinpoint missing or irrelevant context. Its goal is to help AI teams ensure their models perform optimally and continuously improve their AI systems.

Company size
201-500
Company stage
Acquired
Total funding
$131M
Headquarters
Berkeley, CA
Founded
2020

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