AI Scientist - Agentic Verticals

Zoom•Seattle, WA

2d•Hybrid

About The Position

The AI Scientist will develop research and production systems that enable AI agents to improve continuously while protecting customer data privacy. The role owns synthetic data pipelines, advanced evaluation frameworks, and privacy-preserving feedback loops to improve Zoom Virtual Agent and other AI products. Research is expected to ship directly into production and deliver measurable customer impact in a fast-moving, research-to-production environment. The position is part of Zoom’s AI Verticals organization, which works across agent evaluation, model optimization, synthetic data generation, and self-improving systems. It is hybrid and requires on-site presence four days per week at the San Jose, California, or Bellevue, Washington office. Remote arrangements are not available.

Requirements

Master’s degree or higher in Computer Science, Machine Learning, Artificial Intelligence, or a related field with a research focus on autonomous agents, reinforcement learning, self-supervised learning, or LLM-based systems; equivalent practical experience in these areas is also accepted.
Deep hands-on expertise with large language models applied to agent systems or real-world production scenarios.
Strong foundation in machine learning and reinforcement learning, with demonstrated experience in self-improving systems, continual learning, or agents that learn from limited feedback.
Ability to develop novel metrics and frameworks for evaluating agent quality in privacy-constrained environments, beyond standard test sets and LLM-as-judge methods.
Programming proficiency in Python, C++, or Java, and hands-on experience with AI frameworks such as PyTorch or TensorFlow.
Demonstrated ability to translate research innovations into production systems at scale while balancing scientific rigor and practical engineering timelines.
Intellectual curiosity, bias for action, growth mindset, and ability to own ambiguous, open-ended problems and deliver measurable customer impact.

Responsibilities

Research and develop learning mechanisms that allow AI agents to improve continuously while meeting customer data privacy and compliance requirements.
Develop synthetic data generation and simulation methods that capture production error patterns and customer behavior at scale.
Design evaluation frameworks for assessing and improving agent quality at scale in privacy-constrained environments, beyond traditional test sets and LLM-as-judge approaches.
Build scalable production feedback loops using privacy-preserving signals and simulated scenarios to improve agent performance.
Analyze customer needs and business outcomes, including resolution rates, escalation patterns, and conversation quality, to guide research and measure improvements.
Collaborate with engineering teams to deploy research innovations into production, balancing scientific rigor with practical engineering constraints.
Contribute to the research community by publishing findings on self-improving agents, synthetic data generation, and privacy-preserving learning techniques.