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Vector Databases Jobs in Oakland, CA (NOW HIRING)

We build massive-scale infra to crawl the entire web, train state-of-the-art embedding models to process it, and design super high performant vector databases to retrieve over it. We now power search ...

AI Engineer (Mid-Level)

San Francisco, CA · On-site

$180K - $400K/yr

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

AI Engineer (Mid-Level)

San Francisco, CA · On-site

$180K - $400K/yr

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

Contribute to model provider gateways, RAG pipelines, and vector database implementations that keep Ridgeline ahead of the curve in its industry. * Raise the bar. This is an uplevel hire - you'll be ...

Contribute to model provider gateways, RAG pipelines, and vector database implementations that keep Ridgeline ahead of the curve in its industry. * Raise the bar. This is an uplevel hire - you'll be ...

AI Architect

Alameda, CA · On-site

$115 - $135/hr

Design and implement Generative AI solutions including RAG pipelines, vector databases, and AI agents * Establish best practices for AI security, governance, and observability * Evaluate emerging AI ...

... vector databases, search/retrieval systems, and external APIs. • Collaborate with product managers, AI researchers, data engineers, and UX teams to translate high-level agent use cases into robust ...

We build massive-scale infra to crawl the entire web, train state-of-the-art embedding models to process it, and design super high performant vector databases to retrieve over it. We now power search ...

Showing results 21-40

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 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 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 popular job titles related to Vector Databases jobs in Oakland, CA?

For Vector Databases jobs in Oakland, CA, the most frequently searched job titles are:

What job categories do people searching Vector Databases jobs in Oakland, CA look for?

The top searched job categories for Vector Databases jobs in Oakland, CA are:

What cities near Oakland, CA are hiring for Vector Databases jobs?

Cities near Oakland, CA with the most Vector Databases job openings:

Infographic showing various Vector Databases job openings in Oakland, CA as of August 2026, with employment types broken down into 86% Full Time, 10% Part Time, and 4% Contract. Highlights an 81% Physical, 6% Hybrid, and 13% Remote job distribution.

Software Engineer (Java + GenAI)

eFulgent

San Jose, CA • On-site

$60.75 - $83.25/hr

Contractor

Re-posted 6 days ago


Job description

Job Summary (List Format):
- Position: Sr. Software Engineer (Java + GenAI)
- Location: Hybrid role in San Jose, CA
- Duration: 11+ months contract
- Responsibilities:
- 60% focus on software development
- 40% focus on support activities
- Required Skills:
- Strong backend development experience in Java
- Proficiency in Python
- Hands-on experience with Generative AI, including:
- Retrieval-Augmented Generation (RAG)
- Vector databases
- Prompt engineering
- Large Language Models (LLMs)
- Application: Send suitable profiles and contact details to rams@vensoft.com