Research Engineer / Scientist -AI for Databases

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.

• 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.

• PhD in Computer Science, Data Science or a related field with a focus on databases, systems, or machine learning.
• Strong publication record in accredited venues (e.g., SIGMOD, VLDB, ICDE, NeurIPS, etc.) related to the AI4DB area.
• Strong background in database internals (e.g., PostgreSQL, MySQL, or any modern cloud-native databases or BigData platform).
• Hands-on experience with machine learning frameworks (e.g. XGBoost, LightGBM, TensorFlow, PyTorch, scikit-learn).

• Proficiency in Python, C++, or Java.
• Experience with cloud database platforms (AWS, GCP, Azure) is a plus.
• Strong analytical, problem-solving, and communication skills.
• Familiarity with LLM, reinforcement learning, neural architecture search, or automated database tuning.