Our Research Team
Meet the researchers and engineers advancing the frontiers of AI embeddings and retrieval systems.
Principal Researchers
Dr. Sarah Chen
Principal Research Scientist
Multi-Modal Embeddings & Cross-Modal Retrieval
Dr. Chen leads our multi-modal research initiatives with over 8 years of experience in computer vision and natural language processing. She holds a PhD from Stanford and has published 40+ papers in top-tier venues.
Dr. Michael Rodriguez
Senior Research Scientist
Neural Architecture Search & Optimization
Dr. Rodriguez specializes in automated machine learning and neural architecture optimization. Former research scientist at Google Brain with expertise in large-scale ML systems and AutoML.
Dr. Priya Patel
Research Scientist
Semantic Search & Information Retrieval
Dr. Patel focuses on semantic search technologies and information retrieval systems. She has extensive experience in both industry and academia, with a PhD from CMU and prior work at Microsoft Research.
Research Engineers
Specialized Researchers
Dr. Maria Gonzalez
Research Scientist
Scalable Vector Indexing & Algorithms
Dr. Gonzalez researches novel algorithms for large-scale vector indexing and similarity search. Her work focuses on theoretical foundations and practical implementations of hierarchical indexing structures.
Dr. Chen Wei
Research Scientist
Graph Neural Networks & Knowledge Graphs
Dr. Wei explores the intersection of graph neural networks and knowledge representation for enhanced retrieval systems. His research focuses on incorporating structured knowledge into embedding models.
Dr. David Kim
Research Scientist
Privacy-Preserving ML & Federated Learning
Dr. Kim leads research in privacy-preserving machine learning techniques for embedding systems. His work focuses on federated learning approaches that enable collaborative model training without data sharing.
Our Values & Culture
Open Science
We believe in transparent, reproducible research. All our work includes open-source code, detailed documentation, and comprehensive experimental setups.
Collaboration
Great research happens through collaboration. We actively partner with academic institutions, industry labs, and the open-source community.
Impact Focus
Our research aims to solve real-world problems. We prioritize work that can be deployed in production systems and benefit the broader AI community.
Continuous Learning
AI moves fast. We foster a culture of continuous learning, encouraging team members to explore new ideas and stay at the forefront of the field.
Join Our Team
We're always looking for talented researchers and engineers who share our passion for advancing AI technology. Whether you're a seasoned researcher or a promising graduate student, we'd love to hear from you.
Current Openings
- Postdoctoral Researcher - Multi-modal Learning & Cross-modal Retrieval
- Research Engineer - Large-scale Vector Database Systems
- Research Scientist - Federated Learning & Privacy-Preserving ML
- Research Intern - Various projects (Summer 2025)
What We Offer
- Competitive compensation and comprehensive benefits
- Access to state-of-the-art computing resources
- Conference travel and professional development support
- Collaborative, research-focused environment
- Flexible work arrangements and work-life balance