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Vector Databases Jobs in California (NOW HIRING)

Apple

AI Data Engineer

Cupertino, CA

$203K - $305K/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.

Staffingine LLC

AI Architect

Torrance, CA · On-site

$65.75 - $86.75/hr

Develop advanced RAG pipelines leveraging vector databases Chroma DB Milvus FAISS and embedding strategies for contextual accuracy * Integrate AI capabilities with enterprise systems via REST and ...

VBeyond

Java Full Stack AI Developer

Sunnyvale, CA · On-site

$62.50 - $80.50/hr

• Drive the adoption of embedded AI, moving beyond simple API calls to integrating local LLMs and vector databases into the application layer. Evangelize usage of AI tools to accelerate developer ...

Programmers.io

Java Full Stack AI Developer

Sunnyvale, CA · On-site

$61.50 - $79.50/hr

• Drive the adoption of embedded AI, moving beyond simple API calls to integrating local LLMs and vector databases into the application layer. Evangelize usage of AI tools to accelerate developer ...

Aptino

AI Engineer / Developer

Santa Clara, CA · On-site

$133K - $160K/yr

Integrate vector databases and embedding pipelines for AI search and RAG systems Development & Systems * Develop reusable data and AI services using Python and/or other relevant languages * Build ...

Trulioo

Data Platform Engineer

San Diego, CA · On-site

$120K - $150K/yr

Work with Vector Databases (e.g., AWS S3 Vector, PostgresVectorDb, OpenSearch) to support similarity and semantic search applications. * Collaborate with data scientists, software engineers, and ...

Clera

AI Engineer (Mid-Level)

San Francisco, CA · On-site

Own production retrieval-augmented generation (RAG) pipelines and retrieval infrastructure including vector databases, embeddings, and indexing for domain-specific search at scale. * Implement multi ...

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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, Full-Stack - Scale GP

Senior Software Engineer, Full-Stack - Scale GP

Scale AI

San Francisco, CA • On-site

Full-time

Posted 29 days ago

Job description

Job Summary:
Scale AI is an enterprise-grade Generative AI platform providing APIs for knowledge retrieval, inference, evaluation, and more. They are seeking a strong Senior Full-Stack Engineer to help build, scale, and refine their rapidly growing product, working across the stack from front-end to back-end while integrating with machine learning systems.
Responsibilities:
• Own major full-stack product areas, driving features from design through production deployment.
• Build modern frontend experiences using React and TypeScript, ensuring performance, usability, and responsiveness.
• Develop reliable backend services in Python, working with distributed systems, data pipelines, and ML/LLM components.
• Integrate with LLMs, vector databases, and AI infrastructure to power intelligent product experiences.
• Deliver experiments and new features quickly, maintaining high quality and tight feedback loops with customers.
• Collaborate across product, ML, and infrastructure teams to shape the direction of Scale GP.
• Adapt quickly—learning new technologies, frameworks, and tools as needed across the stack.
Qualifications:
Required:
• 5+ years of full-time engineering experience, post-graduation.
• Strong experience developing full-stack applications using React, TypeScript, and Python.
• Experience scaling or shipping products at high-growth startups.
• Familiarity with LLMs, vector databases, embeddings, or other modern AI tooling (tinkering or production experience welcome).
• Proficiency with SQL and modern API development.
• Experience with Kubernetes, containerization, and microservice architectures.
• Experience working with at least one major cloud provider (AWS, GCP, or Azure).
Company:
Scale’s mission is to develop reliable AI systems for the world’s most important decisions. Founded in 2016, the company is headquartered in San Francisco, USA, with a team of 501-1000 employees. The company is currently Late Stage.

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