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Arize AI

AI model observability and evaluation platform

Senior AI Product Engineer - Fullstack

Full-Stack EngineeringConfirmed live in the last 24 hours
Full-timeRemote in USA

About the Job

Requirements

  • At least 5 years of frontend experience building external, user-facing interfaces, preferably with React and TypeScript.
  • Experience debugging complex systems in a team environment.
  • Interest in the AI and large language model ecosystem and willingness to stay current with emerging technologies.
  • Experience building and operating complex, high-volume software-as-a-service platforms or systems.

Responsibilities

  • Write maintainable, scalable, and performant code across the stack, primarily using TypeScript and React, with opportunities to use Python and Go.
  • Design and build application programming interfaces and domain or object models for customers’ machine learning and large language model workflows.
  • Design and build performant, reusable React components for use throughout the application.
  • Research and implement visualization and dimensionality-reduction algorithms in a distributed environment.
  • Collaborate with product, design, and customer engineering teams to enhance and expand product offerings.
  • Contribute to building in-house artificial intelligence agents.
  • Participate in or lead architectural decisions for new and existing features in collaboration with product, design, and backend teams.
  • Contribute to design and code reviews and technical documentation.

Desired Qualifications

  • Experience with GraphQL or a comparable application programming interface technology.
  • Experience with machine learning, analytics, data science, or data visualization products.
  • Working knowledge of machine learning or data science.
  • First-hand experience with large language models or developing artificial intelligence products.

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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