Senior Research Scientist, Operations Research; Infrastructure Lab

Senior Research Scientist, Operations Research (Infrastructure Lab)

Location:

San Jose

Team:
Infrastructure

Employment Type:

Regular

Job Code: A06931

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 NL2
  
  SQL, NL2
  
  Chart.
• 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.

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.

• 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.

• 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.

• Ph.D. degree with strong research achievements, such as multiple first-author papers at conferences (CCF-A) in the areas of Data, Systems, or AI.
• Solid foundation in operations research theory, with expertise in areas such as linear programming, integer programming, and combinatorial optimization.
• Proficient with at least one mainstream commercial or open-source solver (e.g., Gurobi, CPLEX, CP-SAT).
• Familiar with commonly used (meta-)heuristic algorithms (e.g., genetic algorithms, simulated annealing) and experienced in real-world deployment.
• Strong engineering and coding skills, proficient in at least one programming language such as Python, C++, or Java, with solid knowledge of common data structures and algorithms.
• Excellent logical thinking and business abstraction skills, capable of translating ambiguous business requirements into clear technical solutions.
• Strong communication, teamwork, and collaboration abilities.

• Domain knowledge of datacenter hardware supply chain operations or the underlying implementation of cloud computing products.
• Familiarity with LLMs, reinforcement learning, and Agent frameworks such as Lang Graph.
• Experience with prompt engineering optimization and Agent development.
• Strong interest and insight in combining traditional operations research optimization with generative AI.

The base salary range for this position in the selected city is $212,800 - $450,000 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills,…