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Vector Databases Jobs in California (NOW HIRING)

Diverse Lynx

ML/AI Engineer

Burbank, CA

The ideal candidate will have deep expertise in Python, hands-on experience in model training, document processing pipelines, and strong knowledge of vector databases and modern ML/GenAI frameworks.

JConnect Infotech

AI/ML Engineer

Woodland Hills, CA

Vector database integration * Databases * Vector Databases * MongoDB * Production-grade ML Engineering * Scalable, production-ready ML/GenAI solutions Roles & Responsibilities This role is for a ...

JConnect Infotech

Senior AI/ML Engineer

Woodland Hills, CA

$109K - $150K/yr

Vector database integration * Databases * Vector Databases * MongoDB * Production-grade ML Engineering * Scalable, production-ready ML/GenAI solutions Key Responsibilities * Design and implement AI ...

Guru Schools

Sr. Database Engineer

Irvine, CA · On-site

$113K - $154K/yr

Design and implement specialized data storage solutions, such as vector databases (Amazon OpenSearch, pgvector), to support Generative AI and RAG-based applications. * Set the Standards: Define and ...

Diverse Lynx

AI/ML Engineer

Los Angeles, CA

Vector database integration * Databases * Vector Databases * MongoDB * Production-grade ML Engineering * Scalable, production-ready ML/GenAI solutions Roles & Responsibilities * This role is for a ...

Diverse Lynx

Senior AI ML Engineer

Los Angeles, CA

$112K - $154K/yr

... Embedding generation o Vector database integration • Databases o Vector Databases o MongoDB • Production-grade ML Engineering o Scalable, production-ready ML/GenAI solutions Roles ...

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.

Diverse Lynx

AI ML Engineer

Burbank, CA

$111K - $153K/yr

Implement vector search solutions using vector databases * Enable CICD integration and cloud deployment (Azure preferred) * Establish observability| monitoring| and evaluation frameworks

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Vector Databases information

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 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.
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Vector Databases jobs near you

Senior Vector Database Adminsitrator

Purple Drive Technologies

Sunnyvale, CA • On-site

$58.50 - $80.50/hr

Full-time

Posted 17 days ago

Job description

Overview:
Senior Vector Database Adminsitrator
Specializing in Weaviate Vector DB & having GenAI skills
Responsibiliites:
1. Owns reliability, scalability, automation, and uptime of Vector DB services.
2. Evaluate different DB Tech for AI/RAG use cases.
3. Hands-on experience with GenAI foundation models and Weaviate (mandatory), plus evaluation, management, and optimization of embedding storage and high-dimensional indexing (e.g., HNSW).
4. Ensures high-availability, security, backups, and robust performance for production-grade, hybrid-infrastructure AI deployments.

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