Responsibilities
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

• Research and develop new algorithms for approximate nearest neighbor (ANN) search, especially for filtered, hybrid, or disk-based scenarios.
• Optimize existing algorithms for scalability, low latency, memory footprint, and hybrid search support.
• Collaborate with engineering teams to prototype, benchmark, and productionize indexing solutions.
• Contribute to academic publications, open-source libraries, or internal technical documentation.
• Stay current with research trends in vector search, retrieval systems, retrieval-augmented generation (RAG), large language models (LLMs), and related areas.

Qualifications

Minimum Qualifications

• PhD in Computer Science, Applied Mathematics, Electrical Engineering, or a related technical field.
• Strong publication record in top-tier venues (e.g., SIGMOD, VLDB, SIGIR, NeurIPS, ICML, etc.) related to vector search, indexing, IR, or ML.
• Deep understanding of ANN algorithms, quantization, graph-based indexes, and partition-based indexes.
• Strong system-level thinking: ability to profile, benchmark, and optimize performance across CPU, memory, and storage layers.
• Proficiency in C++ and/or Python, with experience in implementing and benchmarking algorithms.

Preferred Qualifications

• Experience building or contributing to vector databases or retrieval engines in production.
• Familiarity with frameworks like FAISS, ScaNN, HNSWLib, or DiskANN.
• Understanding of distributed systems and/or GPU-accelerated search.
• Experience with hybrid search (dense + sparse), multi-modal retrieval, or retrieval for LLMs.
• Passion for bridging theory and practice in production-scale systems.

Job Information
【For Pay Transparency】Compensation Description (Annually)
The base salary range for this position in the selected city is $145000 - $355000 annually.
Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.
Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).
The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

• Research and develop new algorithms for approximate nearest neighbor (ANN) search, especially for filtered, hybrid, or disk-based scenarios.
• Optimize existing algorithms for scalability, low latency, memory footprint, and hybrid search support.
• Collaborate with engineering teams to prototype, benchmark, and productionize indexing solutions.
• Contribute to academic publications, open-source libraries, or internal technical documentation.
• Stay current with research trends in vector search, retrieval systems, retrieval-augmented generation (RAG), large language models (LLMs), and related areas