AI Scientist

Description

AI Scientist - (AI Verticals)

What You Can Expec t

In this role, you will tackle one of the most compelling challenges in production AI: enabling agents to continuously improve without compromising customer data privacy. You will own the development of synthetic data pipelines, advanced evaluation frameworks, and privacy-preserving feedback loops that drive measurable improvements in the company Virtual Agent performance, with your research shipping directly to production and serving real customers across the company’s growing portfolio of AI-powered products. As a part of the AI Verticals organization, you will operate with high autonomy in a fast-moving, research-to-production environment where innovations reach customers significantly faster than traditional research timelines.

This is a hybrid position that requires on-site presence four (4) days per week at our San Jose, CA or Bellevue, WA office. Remote arrangements are not available for this role.

The AI Verticals team sits at the intersection of deep research and real-world production impact, advancing the intelligence and continuous improvement of the company’s AI agent products. Our work spans agent evaluation, model optimization, synthetic data generation, and self-improving systems, with direct influence on the company Virtual Agent and other products across the company portfolio. The team operates a global development model with engineering partners across time zones, enabling research innovations to reach customers faster than traditional research organization timelines.

Zoomies help people stay connected so they can get more done together. We set out to build the best collaboration platform for the enterprise, and today help people communicate better with products like the company Contact Center, the company Phone, the company Events, the company Apps, the company Rooms, and the company Webinars.

We’re problem-solvers, working at a fast pace to design solutions with our customers and users in mind. Find room to grow with opportunities to stretch your skills and advance your career in a collaborative, growth-focused environment.

Our Commitment

At the company, we believe great work happens when people feel supported and empowered. We’re committed to fair hiring practices that ensure every candidate is evaluated based on skills, experience, and potential. If you require an accommodation during the hiring process, let us know—we’re here to support you at every step.

If you need assistance navigating the interview process due to a medical disability, please submit an Accommodations Request Form and someone from our team will reach out soon. This form is solely for applicants who require an accommodation due to a qualifying medical disability. Non-accommodation-related requests, such as application follow-ups or technical issues, will not be addressed.

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Responsibilities

• Research and develop novel learning mechanisms that enable AI agents to continuously improve while adhering to customer data privacy and compliance requirements.
• Develop synthetic data generation and simulation approaches that capture real production error patterns and customer behavior at scale, enabling model improvement through simulated scenarios.
• Design advanced evaluation frameworks that go beyond traditional test sets and LLM-as-judge approaches, addressing how to assess and improve agent quality at scale without compromising customer data privacy.
• Build scalable production feedback loops that leverage privacy-preserving signals and simulated scenarios to drive continuous improvements in agent performance.
• Analyze customer needs and business outcomes, including resolution rate, escalation patterns, and conversation quality, to guide research priorities and deliver measurable improvements in agent performance.
• Collaborate with engineering teams to deploy research innovations into production systems, 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.

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, or equivalent practical experience in these areas.
• Deep hands-on expertise with large language models (LLMs) 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.
• Creative approach to evaluation challenges, with the ability to develop novel metrics and frameworks for agent quality in privacy-constrained environments, beyond standard test sets and LLM-as-judge methods.
• Programming proficiency in Python, C++, or Java, with hands-on experience in AI frameworks such as PyTorch or TensorFlow.
• Demonstrated ability to translate research innovations into production systems at scale, balancing scientific rigor with practical engineering timelines.
• Intellectual curiosity, bias for action, and growth mindset, with the drive to take ownership of ambiguous, open-ended problems and deliver measurable customer impact.

Conditions

$151,800.00 - $332,200.00

In addition to the base salary and/or OTE listed the company has a Total Direct Compensation philosophy that takes into consideration; base salary, bonus and equity value.

Note: Starting pay will be based on a number of factors and commensurate with qualifications & experience.

We also have a location based compensation structure; there may be a different range for candidates in this and other locations

At the company, we offer a window of at least 5 days for you to apply because we believe in giving you every opportunity. Below is the potential closing date, just in case you want to mark it on your calendar. We look forward to receiving your application!

Anticipated Position Close Date:

11/11/26

Ways of Working

Our structured hybrid approach is centered around our offices and remote work environments. The work style of each role, Hybrid, Remote, or In-Person is indicated in the job description/posting.

Benefits

• As part of our award-winning workplace culture and commitment to delivering happiness, our benefits program offers a variety of perks, benefits, and options to help employees maintain their physical, mental, emotional, and financial health; support work-life balance; and contribute to their community in meaningful ways. Click Learn for more information.