Research Scientist

Zoom • Seattle, WA

17d • Hybrid

About The Position

As a Research Scientist on Zoom’s GenAI Science Team, you will play a key role in advancing and applying large language model (LLM) research to enhance AI Companion features such as meeting summaries, intelligent search, and agentic AI capabilities. This role focuses on post-training, reinforcement learning, and model adaptation to improve existing models for real-world product use. You can expect to work in a collaborative, product-focused environment that values both scientific rigor and practical impact. About the Team The GenAI Science Team collaborates closely with Zoom’s AI Incubation team to deliver applied research that powers AI-driven meeting intelligence and productivity features. The team’s mission is to bridge cutting-edge AI research with scalable product delivery, ensuring that Zoom’s AI systems are robust, efficient, and aligned with user needs.

Requirements

• Possess a master’s degree or PHD in Computer Science, Artificial Intelligence, Machine Learning, Distributed Systems, or a related field.
• Have publications in top-tier AI conferences (e.g., ICML, NeurIPS, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV, AAAI).
• Have proficiency in Python, PyTorch and Hugging Face Transformers (required); Verl and Megatron.
• Have LLM post-training techniques such as RLHF, RLVR, reward modeling.
• Have experience in building evaluation systems and benchmarks for AI models, including human-aligned metrics and annotation quality management.
• Demonstrate advanced agent systems with function calls or tool usage.
• Be able to handle long-context modeling or retrieval-augmented generation.

Responsibilities

• Conducting research and development on large language models to support Zoom’s AI Companion and meeting intelligence features.
• Designing and implement model training pipelines, including data creation, filtering, and evaluation workflows.
• Collaborating with cross-functional teams of scientists, engineers, and product managers to translate research into production-ready solutions.
• Exploring and apply advanced post-training techniques such as RLHF, RLVR, and reward modeling.
• Developing evaluation systems and benchmarks to measure model quality, human alignment, and task performance.
• Contributing to the development of agentic AI systems capable of tool usage, function calling, and multi-modal understanding.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees