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Arize AI

AI model observability and evaluation platform

Senior AI Product Manager - Evals & Annotations

Technical Product ManagementPosted on Nov 10
Full-timeRemote in USA

About the Job

Requirements

  • Significant product management experience independently owning technically complex, developer-facing products; exact years are flexible for candidates with exceptional technical depth, product judgment, and ownership.
  • Strong understanding of large language model evaluations and AI development workflows, including automated evaluations, human feedback, experimentation, and how developers iterate on AI applications and agents.
  • Experience with developer tools, AI infrastructure, machine learning platforms, or adjacent technical products, with a solid understanding of backend systems and software architecture.
  • Ability to debug issues independently, navigate code, contribute small fixes or pull requests, build prototypes and technical proofs of concept, propose technical implementations, reason through tradeoffs, and hold credible technical discussions with engineers.
  • Ability to independently define and prototype core product experiences and work with designers and design engineers to refine and ship them.
  • Strong ownership, initiative, bias toward action, and comfort navigating ambiguity and making tradeoffs while working hands-on without a fully defined roadmap or direction.
  • Ability to work collaboratively with founders, engineers, and designers, engage in constructive debate, and operate in an environment where responsibilities overlap.
  • Experience working closely with technical customers and communicating effectively while moving between strategy and execution.

Responsibilities

  • Own the Evals & Annotations product area end-to-end, including evaluation creation and execution, human review, annotation workflows, and the broader evaluation lifecycle.
  • Set product strategy and roadmap, balancing customer needs, foundational platform investments, user experience improvements, and emerging opportunities in AI and agent development.
  • Shape the evaluation experience by developing product and workflow opinions, independently prototyping solutions, and partnering with Design to create intuitive, polished experiences.
  • Shape the future of evaluations as AI development evolves toward autonomous and managed agents, including opportunities within the Dynatrace ecosystem.
  • Work with Engineering on technically complex problems spanning evaluation execution, backend systems, orchestration, and data infrastructure; contribute to technical discussions, implementation approaches, and tradeoffs.
  • Speak with customers multiple times each week to understand how they evaluate and improve AI systems, uncover underlying problems, and validate product direction.
  • Collaborate with cofounders and cross-functional teams on product direction, design exploration, and prioritization, using independent judgment to turn collaborative thinking into execution.
  • Make prioritization decisions across competing demands and drive products from problem definition through launch, adoption, and iteration.

Desired Qualifications

  • Direct experience with large language model evaluation platforms, annotation systems, human-in-the-loop workflows, or agent orchestration.
  • A strong backend or platform engineering background, particularly with distributed systems, workflow orchestration, or data infrastructure.
  • Hands-on programming experience and comfort working across backend and frontend technologies.
  • Experience with large language model-as-a-judge, custom evaluators, experiment tracking, datasets, or feedback-driven improvement workflows.
  • Previous engineering experience or substantial hands-on software development experience.
  • Early-stage or high-growth startup experience, particularly in founder-led environments.

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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