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Vector Databases Jobs in California (NOW HIRING)

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 ...

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.

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 ...

The ideal candidate will have hands-on experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, vector databases, and cloud-native AI ...

RAG Architecture & Vector Databases * AI Agents & Conversational AI * LangChain / LlamaIndex / AutoGen * Backend & API Development * Cloud Technologies (AWS/GCP/Azure) * Docker / Kubernetes ...

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 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.
What cities in California are hiring for Vector Databases jobs? Cities in California with the most Vector Databases job openings:
Infographic showing various Vector Databases job openings in California as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Staff+ Software Engineer, Databases

Anthropic

San Francisco, CA • On-site

$320K - $485K/yr

Full-time

PTO

Re-posted 20 days ago


Job description

About Anthropic
Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the Role
We're looking for experienced engineers to build and scale the database infrastructure that powers Claude's products and Anthropic's research. As a Software Engineer on the Databases team, you'll architect and operate the systems that let millions of users interact with Claude while also supporting frontier AI research workloads.
You'll help set the database strategy for Anthropic: designing systems that handle billions of API requests, building storage that runs cleanly across GCP, AWS, and a range of deployment models, and creating the reliable data layer that lets research move fast.
The Databases team spans three areas: the core database platform (data plane and control plane), data movement (migrations, backfill, and change data capture), and caching. We're hiring across all three.
Key Responsibilities
  • Drive the technical direction for database solutions used across Product and Research
  • Design and implement database solutions that scale to support millions of users across Claude's product ecosystem
  • Build and scale database systems through 100x+ growth while maintaining reliability and performance
  • Build the database platform that lets Anthropic engineers ship without thinking about databases or scaling.
  • Architect data storage solutions that operate across GCP, AWS, first-party deployments, third-party deployments, and other environments
  • Develop database infrastructure that serves both product and research workloads with different performance characteristics
  • Build data movement infrastructure (migration tooling, backfill, and change data capture pipelines) that safely consolidates and moves data across the organization
  • Design and operate caching infrastructure, including CDC-driven cache invalidation, that keeps Anthropic's hottest paths fast and correct
  • Partner with product and research teams to understand data requirements and build infrastructure that accelerates their work
  • Optimize database performance, reliability, and cost efficiency at scale
  • Make build-vs-buy decisions for database technologies
Minimum Qualifications
  • Significant experience as a software engineer building and operating production database or storage systems
  • Deep knowledge of distributed database architectures and OLTP systems at scale
  • Proficiency with SQL and at least one major relational or distributed database engine (e.g., PostgreSQL, MySQL, Spanner, CockroachDB, DynamoDB)
  • Track record of leading large, complex infrastructure projects as an engineer or tech lead
  • Ability to balance moving quickly with the reliability needs of production systems
  • Strong technical leadership and cross-functional collaboration skills
Preferred Qualifications
  • 10+ years building and scaling database systems, with 3+ years leading large-scale projects or teams
  • Experience scaling databases through periods of rapid growth at high-growth companies
  • Experience operating Spanner, CockroachDB, TiDB, AlloyDB, or other globally distributed SQL databases in production
  • Experience with Redis, Temporal, vector databases, or async job processing frameworks
  • Experience with change data capture (Debezium or similar), large-scale data migration, or streaming data infrastructure
  • Experience building multi-cloud or hybrid cloud database solutions
  • Knowledge of database orchestration and automation at scale
  • Contributions to database internals, storage engines, or related open source projects

Note: Prior AI/ML infrastructure experience is not required. We value deep infrastructure and database expertise from any domain.
Deadline to apply: None. Applications will be reviewed on a rolling basis.
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role's On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary:
$320,000-$485,000 USD
Logistics
Minimum education: Bachelor's degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links-visit anthropic.com/careers directly for confirmed position openings.
How we're different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact - advancing our long-term goals of steerable, trustworthy AI - rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.