About the Team
The Infrastructure System Lab is a hybrid research and engineering team dedicated to building the next generation of AI-native data infrastructure.
Operating at the crossroads of databases, large-scale systems, and AI, the team innovates across multiple domains, including advanced VectorDBs and multi-modal databases for large-scale retrieval and reasoning, intelligent infrastructure optimization using machine learning, LLM-based developer tools like
NL2SQL and NL2Chart, and high-performance cache systems for distributed storage and LLM inference.
The lab is deeply collaborative, with researchers and engineers working side by side to turn groundbreaking ideas into production-ready systems.
Their work is deployed at scale, powering products used by millions, and frequently shared through publications and open-source contributions.
About the Role
We are looking for a passionate and skilled professional to join our
AI4DB team, where artificial intelligence meets cutting-edge database technology.
In this role, you will design and implement intelligent systems that improve the performance, scalability, and usability of modern databases.
Your work will span query optimization, indexing strategies, workload forecasting, and the development of self-managing databases.
This role offers the chance to solve complex, high-impact problems at the intersection of AI, systems, and software engineering, while collaborating with a top-tier team.
You'll have opportunities to publish, contribute to open-source, attend leading conferences, and benefit from competitive compensation, strong research support, and an innovation-driven environment.
Responsibilities
- Conduct research and development in applying AI/ML techniques to database management systems.
- Develop intelligent algorithms for tasks such as query planning, indexing, storage management, and workload prediction/scheduling.
- Collaborate with data infrastructure and engineering teams to integrate AI models into production systems.
- Analyze large-scale datasets from database workloads to uncover optimization opportunities.
- Publish findings in top-tier conferences and journals (VLDB, SIGMOD, ICDE, NeurIPS, etc.).
- Contribute to open-source projects or internal tools supporting AI-enhanced databases.