Arize AI
Open Source AI Engineer (Typescript)
About this role
Arize AI seeks an Open Source AI Engineer to develop and maintain frameworks and tooling for LLM observability and evaluation. You'll architect open-source libraries, collaborate with the developer community, and help teams monitor and improve AI systems in production.
What you'll do
- Design and build open-source LLM observability frameworks, libraries, and APIs
- Engage with the open source community through code review, feedback collection, and project direction
- Prototype and experiment with advanced LLM techniques and translate research into developer tools
- Integrate observability features to surface insights on LLM behavior and help diagnose issues like hallucinations
- Create technical documentation, blog posts, tutorials, and educational content
- Iterate rapidly on solutions based on real-world developer needs and feedback
What they're looking for
- TypeScript and isomorphic JavaScript/Node.js development
- LLM frameworks and prompt engineering techniques
- Open source software development and community collaboration
- ML observability, debugging, and evaluation metrics
- API design and architecture
- Technical writing and developer advocacy
Benefits
- Shape the future of AI evaluation and observability
- High autonomy and ownership of major initiatives
- Fully remote work with flexible environment
- Work alongside leading enterprise customers including Fortune 500 companies
- Access to Bay Area and NYC offices for optional in-person collaboration
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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 an open source project you've contributed to—what drew you to it and what impact did your contributions have?
- Describe your experience with LLM frameworks and prompt engineering. Which tools do you prefer and why?