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Pinecone Vector Databases Jobs in California (NOW HIRING)

Relanto

Full Stack Architect - Agentic AI

Fremont, CA · On-site

$150 - $200/hr

Implement and optimize vector database integrations (e.g., Pinecone, Weaviate, FAISS) and embedding pipelines on GCP. * Define and enforce best practices in cloud-native DevOps, microservices, and CI ...

New

ClinDCast LLC

Python Backend Engineer

San Jose, CA · On-site

$100 - $150/hr

Experience with vector databases such as Pinecone, Weaviate, or similar technologies. * Proficiency with Docker and cloud platforms (AWS or GCP). * Experience with asynchronous programming and task ...

Broadcom Inc.

$119 - $190/hr

Expert knowledge of vector databases (Pinecone, Weaviate, Milvus) and scalable architecture principles. * High proficiency in Kubernetes, Docker, and modern CI/CD pipelines to ensure AI agents are ...

New

ClinDCast LLC

Python Backend Engineer

Sunnyvale, CA · On-site

$60 - $65/hr

Experience with Vector Databases such as Pinecone, Weaviate, or similar technologies. Proficiency with Docker and cloud platforms (AWS or GCP). Experience with asynchronous programming and task ...

Hop

AI/ML Engineer

San Francisco, CA · On-site

Responsibilities : • Build and maintain production-grade LLM pipelines and agentic workflows. • Design and optimize RAG architectures using vector databases (Pinecone, FAISS, Weaviate) at scale ...

Hop

AI/ML Engineer

San Francisco, CA · On-site

Design and optimize RAG architectures using vector databases (Pinecone, FAISS, Weaviate) at scale. * Implement agentic systems using LangGraph, LlamaIndex, or equivalent: tool use, multi-agent ...

Info Way Solutions

Golang

San Jose, CA · On-site

... vector databases such as pgvector, Pinecone, or Weaviate, and search APIs. • Solid knowledge of RESTful API design and distributed systems. • Demonstrated ability to work effectively as a team ...

Hop

AI/ML Engineer

San Francisco, CA · On-site

Design and optimize RAG architectures using vector databases (Pinecone, FAISS, Weaviate) at scale. Implement agentic systems using LangGraph, LlamaIndex, or equivalent: tool use, multi-agent ...

Everpure

Software Engineer

Santa Clara, CA · On-site

Familiarity with vector databases such as Pinecone, Weaviate, OpenSearch, or similar. * Experience with CI/CD pipelines and containerized deployments using Docker and Kubernetes. * Exposure to ...

Resource Logistics Inc.

Python Engineer

San Jose, CA · On-site

... Vector Databases (Pinecone, Weaviate) Company : Resource Logistics is an information technology company providing ERP, CRM implementation, software solutions, and staffing services. Founded in 1997 ...

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Pinecone Vector Databases information

What is a Pinecone vector database?

A Pinecone Vector Database is a cloud-based service designed to efficiently store, index, and search high-dimensional vector data, such as embeddings generated by machine learning models. It enables fast similarity search, making it ideal for use cases like semantic search, recommendation systems, and AI-powered applications. Pinecone handles the complexity of scaling and managing vector data, so developers can focus on building intelligent applications without worrying about infrastructure.

What are the key skills and qualifications needed to thrive as a Pinecone vector database engineer, and why are they important?

To thrive as a Pinecone Vector Database Engineer, you need a strong background in computer science, data engineering, and experience with large-scale distributed systems, often supported by a relevant degree or equivalent experience. Proficiency in Python, REST APIs, cloud platforms (AWS, GCP), and vector search technologies, along with familiarity with Pinecone’s SDK and database management, are commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help you collaborate with cross-functional teams and deliver scalable solutions. These skills ensure robust database performance, efficient data retrieval, and successful integration of vector search capabilities into real-world applications.

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

Engineers working with Pinecone Vector Databases often encounter challenges such as optimizing vector search performance at scale, ensuring data consistency across distributed systems, and integrating the database with various machine learning pipelines. Addressing these challenges typically involves tuning indexing parameters, monitoring resource utilization, and collaborating closely with data scientists to understand retrieval requirements. Regularly reviewing documentation and participating in community forums can also help engineers stay current with best practices and new features.

What is the difference between Pinecone Vector Databases vs Data Engineers?

AspectPinecone Vector DatabasesData Engineers
Primary RoleManaging and deploying vector database solutions for AI/ML applicationsDesigning, building, and maintaining data pipelines and infrastructure
Skills & CertificationsKnowledge of vector databases, cloud platforms, programming (Python, SQL)Data modeling, ETL processes, cloud services, programming (Python, Java)
Work EnvironmentTech companies, AI startups, cloud providersData-driven organizations, tech firms, finance, healthcare

While Pinecone Vector Databases specialists focus on deploying and managing vector database solutions for AI applications, Data Engineers build and maintain the data infrastructure that supports these systems. Both roles require programming skills and familiarity with cloud platforms, but their core responsibilities differ: one centers on database management, the other on data pipeline development.

What are popular job titles related to Pinecone Vector Databases jobs in California?

For Pinecone Vector Databases jobs in California, the most frequently searched job titles are:

What job categories do people searching Pinecone Vector Databases jobs in California look for?

The top searched job categories for Pinecone Vector Databases jobs in California are:

What cities in California are hiring for Pinecone Vector Databases jobs?

Cities in California with the most Pinecone Vector Databases job openings:

Pinecone Vector Databases jobs near you
Infographic showing various Pinecone Vector Databases job openings in California as of August 2026, with employment types broken down into 87% Full Time, 8% Part Time, 1% Temporary, and 4% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution.

Staff Software Engineer, Applied AI

Emergence Capital Partners

San Francisco, CA • On-site

Full-time

Re-posted 13 days ago

Job description

About the Role
We're building AI products that solve real problems for real customers. This role sits at the intersection of product and engineering- you'll own features end-to-end, from understanding user problems to shipping production code that scales.
Applied AI product engineering is a new discipline. You won't just be integrating APIs - you'll be designing AI systems that work reliably in production, building evaluation frameworks to measure quality, and iterating rapidly based on real user feedback. You'll work directly with customers to understand their workflows and translate that understanding into product decisions.
This is not a research role. We're looking for engineers who ship. You'll be measured by the products you deliver and the problems you solve, not by papers published or models trained. The best candidates combine deep technical skills with strong product intuition and a relentless focus on user outcomes.
What You'll Do
  • Design, build, and ship AI-powered product features from concept through production deployment
  • Develop and maintain LLM-based systems including RAG pipelines, agents, and workflow automation
  • Build evaluation frameworks and monitoring systems to ensure AI quality and reliability
  • Work directly with customers to understand problems, gather feedback, and validate solutions
  • Collaborate with product and design to define roadmap and prioritize features based on impact
  • Optimize system performance, cost, and latency as usage scales
  • Contribute to technical architecture decisions and establish engineering best practices

Qualifications
  • 4+ years of software engineering experience, with at least 1-2 years building AI/ML-powered products
  • Strong proficiency in Python; experience with TypeScript/JavaScript for full-stack development
  • Hands-on experience with LLMs in production: prompt engineering, RAG systems, fine-tuning, or agent frameworks
  • Solid understanding of ML fundamentals: embeddings, vector databases, evaluation metrics, and model selection
  • Experience building and maintaining production systems with high reliability requirements
  • Strong product intuition, ability to translate ambiguous user needs into concrete technical solutions
  • Excellent communication skills and comfort working directly with customers and stakeholders
  • BS/MS in Computer Science, or equivalent practical experience

Nice to Have
  • Experience with AI infrastructure: LangChain, LlamaIndex, vector databases (Pinecone, Weaviate, Chroma)
  • Background in NLP, information retrieval, or search systems
  • Previous startup experience, especially as an early engineer or technical co-founder
  • Domain expertise in financial services, legal, healthcare, or other enterprise SaaS verticals
  • Contributions to open-source AI/ML projects
  • Experience building developer tools or platforms

Apply