1

Pinecone Vector Databases Jobs (NOW HIRING)

AI/ML Engineer

Plano, TX · On-site

$65 - $75/hr

Deep operational expertise with vector databases, such as Pinecone, Milvus, Weaviate, or Qdrant. * AWS Solutions Architect, AWS DevOps Engineer, or equivalent industry certifications.

AI/ML Engineer

Plano, TX · On-site

$120 - $160/hr

Deep operational expertise with vector databases, such as Pinecone, Milvus, Weaviate, or Qdrant. * AWS Solutions Architect, AWS DevOps Engineer, or equivalent industry certifications.

... vector databasesPinecone • FAISS • MLOps Systems • MLflow • model registry • CICD pipelines • Experiment tracking and automated testing • Deployment patterns • batch real-time ...

Build and implement RAG pipelines with vector databases (e.g., Pinecone, FAISS). Develop Generative AI solutions, including chatbots, summarization, and content creation tools. Preprocess, clean, and ...

... vector databases like Pinecone, Weaviate, Milvus, or FAISS • Expertise in APIs, REST, and cloud platforms (AWS, Azure, GCP) • Knowledge of distributed systems and workflow automation tools ...

next page

Showing results 1-20

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.

More about Pinecone Vector Databases jobs

What cities are hiring for Pinecone Vector Databases jobs?

Cities with the most Pinecone Vector Databases job openings:

What states have the most Pinecone Vector Databases jobs?

States with the most job openings for Pinecone Vector Databases jobs include:

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

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

Infographic showing various Pinecone Vector Databases job openings in the United States as of August 2026, with employment types broken down into 89% Full Time, 6% Part Time, and 5% Contract. Highlights an 81% Physical, 6% Hybrid, and 13% Remote job distribution.

Senior/Staff Software Engineer, Search & Retrieval Infrastructure

Manhattan, NY • On-site

Pinecone
Software Development • 11 - 50 employees

$180 - $250/hr

Other

Medical, Dental, Vision, Retirement

Posted 22 days ago


Job description

About Pinecone

Pinecone is the knowledge infrastructure for AI at scale. Its leading vector database and knowledge engine, Pinecone Nexus, power accurate, performant AI applications for more than 9,000 customers and 800,000 developers worldwide. Pinecone's mission is to make AI knowledgeable. Pinecone is based in New York and raised $138M in funding from Andreessen Horowitz, ICONIQ, Menlo Ventures, and Wing Venture Capital.

About the Team and Role:

We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability.

Responsibilities:
  • Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata‑aware search, and LLM generation
  • Design and build optimized indexing pipelines for structured and unstructured data
  • Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration
  • Improve retrieval quality through evaluation and observability frameworks
  • Design APIs for internal and external user and agentic consumers
  • Optimize latency, throughput and cost across large-scale inference and retrieval workloads
  • Drive technical direction for reliability and security
What You’ll Bring to the Table:

To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge.

Systems Expertise
  • Architectural Depth: You have a proven track record (typically 6+ years) of shipping production‑grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long‑term maintainability.
  • Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas.
AI & Retrieval
  • Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch.
  • RAG & Orchestration: You understand the nuances of Retrieval‑Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance.
Technical
  • Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python.
  • Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud‑native architectures, and observability frameworks, as well as infrastructure‑as‑code tools like Terraform or Pulumi.
Ownership & Impact
  • Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love.
  • Ambiguity Navigator: You’re comfortable in a high‑growth environment. You prefer "owning a problem" over "executing a ticket."
Bonus Points
  • Experience building multi‑tenant SaaS platforms.
  • Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results.
  • Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps).
Perks & Benefits:
  • Comprehensive health coverage including medical, dental, vision, and mental health resources
  • 401(k) Plan
  • Equity award
  • Flexible time off
  • Paid parental leave
  • Annual Company Retreat
  • WFH Equipment Stipend

All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status and any other legally protected status under federal, state, or local anti‑discrimination laws.

#J-18808-Ljbffr