ZipRecruiter

Log In

1

Vector Databases Jobs in California (NOW HIRING)

Apple

AI Data Engineer

Cupertino, CA · On-site

$141K - $169K/yr

You will design and implement data pipelines that ingest from legal systems, transform data into AI-ready formats, load vector databases and other AI stores, and expose data services through APIs.

Kai Cyber, Inc.

Senior Data Engineer, AI Platform

San Jose, CA · On-site

$124K - $168K/yr

Familiarity with vector databases and ANN search systems * Experience in data systems for AI platforms or ML infrastructure * Background in search, recommendation systems, or information retrieval ...

next page

Showing results 1-20

All JobsVector Databases JobsVector Databases Jobs in California

Vector Databases information

What is the salary of a vector database developer?

The salary of a vector database developer typically ranges from $80,000 to $150,000 annually, depending on experience, location, and company size. Skilled developers with expertise in machine learning, data structures, and database management may earn higher salaries, especially in tech hubs or with advanced certifications.

Are vector databases the future?

Vector database jobs involve managing and optimizing databases designed for high-dimensional vector data, which are essential for AI and machine learning applications. As AI continues to grow, demand for professionals skilled in vector database technologies and related tools like embedding models is expected to increase, making this a promising field for future job opportunities.

What are vector databases?

Vector databases are specialized databases designed to store, manage, and search high-dimensional vector data, which is commonly generated from machine learning models, such as embeddings from natural language processing or image recognition. They enable efficient similarity search operations, such as finding the most similar items to a given query vector, which is essential for applications like recommendation systems, semantic search, and AI-powered search engines. Unlike traditional databases that handle structured or unstructured data, vector databases are optimized for fast and scalable similarity searches on large datasets of vectors.

What are some common challenges faced when working with vector databases, and how can they be addressed?

Professionals working with vector databases often encounter challenges such as efficiently scaling to handle large datasets, ensuring low-latency similarity searches, and integrating the database with machine learning pipelines. To address these, teams typically implement distributed architectures, fine-tune indexing strategies, and collaborate closely with data engineers and machine learning specialists. Staying updated with the latest developments in vector database technologies and maintaining clear communication with cross-functional teams are also key to overcoming these challenges.

What is the difference between Vector Databases vs Data Engineers?

AspectVector DatabasesData Engineers
Required SkillsDatabase management, data modeling, query optimizationData pipeline development, ETL processes, programming
Work EnvironmentData storage systems, AI/ML projects, cloud platformsData infrastructure, cloud environments, big data tools
Industry UsageAI, machine learning, recommendation systemsData integration, analytics, data architecture

While Vector Databases focus on storing and querying high-dimensional vector data for AI applications, Data Engineers build and maintain data pipelines and infrastructure to support data analysis and machine learning workflows. Both roles are essential in data-driven industries but serve different functions within the data ecosystem.

What can you do with a vector database?

A vector database is used in roles involving data management and machine learning to store, search, and retrieve high-dimensional vector representations of data such as images, text, or audio. It enables efficient similarity searches, supporting applications like recommendation systems, natural language processing, and computer vision. Working with a vector database often requires knowledge of data structures, indexing techniques, and programming skills in languages like Python or C++.

What are the key skills and qualifications needed to thrive as a Vector Database Engineer, and why are they important?

Success as a Vector Database Engineer requires a strong background in computer science, database management, and experience with machine learning or AI-driven data systems. Familiarity with vector database platforms (such as Pinecone, Milvus, or Weaviate), cloud infrastructure, and proficiency in languages like Python are typically expected. Strong problem-solving skills, effective communication, and the ability to work cross-functionally help engineers stand out. These competencies are vital to efficiently design, deploy, and maintain scalable vector search solutions that power modern AI applications.

What are the top 5 vector databases?

Top vector databases used in data management and AI applications include Pinecone, Weaviate, FAISS, Milvus, and Annoy. These databases are optimized for storing and searching high-dimensional vector data, often requiring skills in machine learning and database management. They are widely adopted for tasks like similarity search and recommendation systems.
What are popular job titles related to Vector Databases jobs in California? For Vector Databases jobs in California, the most frequently searched job titles are:
What job categories do people searching Vector Databases jobs in California look for? The top searched job categories for Vector Databases jobs in California are:
What cities in California are hiring for Vector Databases jobs? Cities in California with the most Vector Databases job openings:
Vector Databases jobs near you
Senior Software Engineer, Vector Index Research

Senior Software Engineer, Vector Index Research

Zilliz

Redwood City, CA • On-site

$175K - $250K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 12 days ago

Job description

Zilliz is a fast-growing startup developing the industry's leading vector database for enterprise-grade AI. Founded by the engineers behind Milvus, the world's most popular open-source vector database, the company builds next-generation database technologies to help organizations quickly create AI applications. On a mission to democratize AI, Zilliz is committed to simplifying data management for AI applications and making vector databases accessible to every organization.
The Vector Index team focuses on building the core vector retrieval capabilities behind Milvus, Zilliz Cloud, and Vector Lakebase. We work on making similarity search over massive embedding datasets faster, more accurate, and more cost-efficient, while continuously advancing ANN algorithms, index structures, quantization, compression, recall optimization, CPU/GPU acceleration, and high-performance retrieval frameworks.
This role sits at the intersection of research and engineering. You will read papers, evaluate new algorithms, build prototypes, and turn promising ideas into production-grade vector indexing and retrieval systems. We are looking for engineers who enjoy research, but also have strong engineering fundamentals, performance optimization skills, and engineering taste.
What you'll do:
  • Research, evaluate, and implement new vector indexing and retrieval algorithms for Milvus, Zilliz Cloud, and Vector Lakebase
  • Read papers and track emerging work in vector search, ANN algorithms, index structures, quantization, compression, reranking, GPU acceleration, and AI retrieval systems
  • Build high-performance vector indexing components, including index building, query paths, vector preprocessing, quantization, compression, memory layout, and CPU/GPU acceleration
  • Optimize vector retrieval performance across latency, throughput, recall, memory usage, index build time, and cost efficiency
  • Design benchmarks and evaluation frameworks to compare algorithms and implementations under real data scale, real query patterns, and real AI workloads
  • Debug and solve complex performance issues across algorithm implementation, CPU/GPU execution, SIMD/vectorization, memory access, concurrency, and I/O
  • Turn research prototypes into maintainable, testable, and evolvable production-grade indexing capabilities
  • Use AI tools across the research and engineering workflow, including paper analysis, prototype generation, code implementation, testing, benchmarking, documentation, and performance analysis

What we're looking for:
  • 3+ years of experience in vector search, ANN algorithms, search systems, high-performance computing, or performance-critical systems
  • Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience
  • Strong C++ or Rust programming ability and solid engineering fundamentals
  • Experience with vector similarity search, ANN algorithms, index structures, quantization, compression, reranking, or high-performance retrieval systems is a strong plus
  • Strong interest in research-driven engineering: reading papers, evaluating tradeoffs, building prototypes, and turning ideas into production systems
  • Experience with performance optimization and systematic debugging is a strong plus, especially around CPU/GPU execution, SIMD, memory layout, concurrency, I/O, or large-scale data processing
  • Interest in using AI tools to improve research, coding, testing, benchmarking, documentation, and performance analysis

How we operate:
  • Research-driven, production-focused: We track frontier algorithms, but care most about whether they work under real data scale, real query patterns, and real production constraints
  • Extreme performance: We care about every memory access, every query path, and every tradeoff between recall and latency
  • AI-first engineering: We actively use AI to accelerate paper reading, prototyping, coding, testing, documentation, and performance analysis, but human judgment and engineering taste still matter most
  • Fast and pragmatic: We work on hard vector indexing and retrieval problems, but we ship them into Milvus, Zilliz Cloud, and Vector Lakebase
  • Open source by default: Milvus is a core part of our engineering culture, and strong indexing capabilities should stand up to public design, code, and community usage

Benefits:
  • Competitive compensation (cash + equity)
  • Regular bonus and equity refresh opportunities
  • Medical, dental, and vision insurance
  • Paid time off, including vacation, sick leave, and global reset/wellbeing days
  • Generous 401(k) and regional retirement plans

$175,000 - $250,000 a year
Zilliz is an Equal Opportunity Employer and welcomes people from all backgrounds, experiences, abilities, and perspectives. All qualified applicants will receive consideration for employment regardless of race, color, national origin, religion, sexual orientation, gender, gender identity, age, physical disability, or length of time spent unemployed.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Apply