About the Team We are the Infrastructure System Lab — a hybrid research and engineering group building the next-generation AI-native data infrastructure. Our work sits at the intersection of databases, large-scale systems, and AI. We drive innovation across: - Next-generation databases: We build VectorDBs and multi-modal AI-native databases designed to support large-scale retrieval and reasoning workloads. - AI for Infra: We leverage machine learning to build intelligent algorithms for infrastructure optimization, tuning, and observability. - LLM Copilot: We develop LLM-based tooling like NL2SQL, NL2Chart. - High-performance cache systems: We develop a multi-engine key-value store optimized for distributed storage workloads. We're also building KV caches for LLM inference at scale.
This is a highly collaborative team where researchers and engineers work side-by-side to bring innovations from paper to production. We publish, prototype, and build robust systems deployed across key products used by millions.
About the Role We are seeking a highly motivated and technically strong Research Scientist with a PhD in Computer Science, Database, Information Retrieval, or a related field to join our team. You will work on designing and optimizing state-of-the-art vector indexing algorithms to power large-scale similarity search, filtered search, and hybrid retrieval use cases.
Your work will directly contribute to the next-generation vector database infrastructure that supports real-time and offline retrieval across billions or even trillions of high-dimensional vectors.
Why Join Us - Work on problems at the frontier of AI x systems with huge practical impact. - Collaborate with a world-class team of researchers and engineers. - Opportunity to publish, attend conferences, and contribute to open-source. - Competitive compensation, generous research support, and a culture of innovation.
Responsibilities - For scenarios such as AI data centers and cloud resource scheduling, understand business requirements, formulate mathematical models, and design and develop efficient algorithms, heuristic algorithms, and meta-heuristic algorithms for optimization problems. - Explore AI for OR by integrating LLM, RL and Agent technologies into the operations research optimization pipeline, including but not limited to: Natural language-based decision engine interfaces & Enhancing the interpretability of optimization results
The base salary range for this position in the selected city is $202160 - $368220 annually.
