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Vector Databases Jobs in Seattle, WA (NOW HIRING)

DigitalOcean

Staff Product Manager

Seattle, WA · Hybrid

$186K - $233K/yr

Lead development of next-generation data primitives - including vector databases, knowledge bases, MCP integrations, and open table formats like Apache Iceberg - to power RAG workflows and agentic ...

Programmers.io

Azure AI/ML Engineer

Bellevue, WA · On-site

$62 - $77/hr

... experience with Vector Databases and embedding-based search e.g. Azure AI SearchPractical experience with Semantic Kernel| AI Foundry| Lang Chain| LlamaIndex| or similar frameworks| Azure ...

Stoke Space

Senior AI Software Engineer

Kent, WA · On-site

$138K - $182K/yr

... vector databases to enable intelligent search and retrieval • Develop comprehensive evaluation frameworks (evals) to measure, monitor, and improve AI system performance, accuracy, and reliability ...

WRITER

Software engineer, agents

Seattle, WA · On-site

... 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 ...

Zilliz

Enterprise Account Executive - Seattle

Seattle, WA · On-site +1

$100K - $175K/yr

Zilliz is a fast-growing startup developing the industry's leading vector database for enterprise-grade AI. Founded by the engineers behind Milvus, the world's most popular open-source vector ...

Zilliz

Enterprise Account Executive - Seattle

Seattle, WA · On-site

$100K - $175K/yr

Zilliz is a fast-growing startup developing the industry's leading vector database for enterprise-grade AI. Founded by the engineers behind Milvus, the world's most popular open-source vector ...

Zilliz

Enterprise Account Executive - Seattle

Seattle, WA · Remote

$100K - $175K/yr

Zilliz is a fast-growing startup developing the industry's leading vector database for enterprise-grade AI. Founded by the engineers behind Milvus, the world's most popular open-source vector ...

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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 are popular job titles related to Vector Databases jobs in Seattle, WA? For Vector Databases jobs in Seattle, WA, the most frequently searched job titles are:
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What cities near Seattle, WA are hiring for Vector Databases jobs? Cities near Seattle, WA with the most Vector Databases job openings:
Vector Databases jobs near you
Infographic showing various Vector Databases job openings in Seattle, WA as of August 2026, with employment types broken down into 87% Full Time, 5% Part Time, 1% Temporary, and 7% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution.

Data Engineering Manager (Hands-On / Python)

Search Solutions

Seattle, WA • Remote

$160K - $200K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

This job post has expired today. Applications are no longer accepted.

Job description

The Manager of Data Engineering is a hands-on role and will be the primary architect and technical lead for the data infrastructure powering our next-generation Agentic AI products. Acting as a hands-on leader, you are responsible for the team’s overall delivery, translating complex product requirements into actionable technical tasks for a small engineering squad. You will design the multi-modal data stores (Vector and Graph) that serve as the "Active Memory" for autonomous agents while remaining deeply embedded in the codebase to drive execution.

Key Responsibilities

● Technical Execution: Lead the technical delivery by translating high-level product roadmaps into actionable development cycles. You will own the task breakdown and manage the workflow to ensure high-quality output from the team

● Strategic Data Architecture: Architect and directly implement multi-modal data pipelines that process structured parts catalogs and unstructured sources (PDFs, Word, PNG/SVG diagrams) into specialized Vector and Graph data stores.

● Knowledge Layer Development: Design and optimize the storage of embeddings in Vector Databases (e.g., Pinecone, ChromaDB, Vertex AI Search) and Graph Databases (e.g., FalkorDB, Neo4j) to enable multi-step agentic reasoning across disparate data sources.

● Agent-Driven Development: Deeply integrate autonomous coding agents into your daily workflow to plan, generate, and refactor data infrastructure and microservices.

● Evaluation Pipelines: Collaborate with AI Engineers to build "Gold Dataset" pipelines used for the automated verification, retrieval quality measurement, and "confidence scoring" of AI outputs.

● Microservices: Develop and maintain scalable data services and RESTful APIs using Python (FastAPI/Django) to provide structured, validated data.

● Cloud Operations: Deploy and monitor high-scale data workloads on GCP (Vertex AI, BigQuery), ensuring system reliability, security, and cost-effectiveness


Skills and Experience


10+ years of Software/Data Engineering experience, with a proven history of leading technical teams.

Proven track record of designing and building production-grade AI data systems.


Technical Stack: Advanced Python (FastAPI, Pydantic, SQLAlchemy) and SQL mastery for building scalable microservices.

AI: Hands-on experience with Vector Databases (Pinecone, ChromaDB), RAG pipelines, and GraphRAG patterns.

Data Tooling: Deep experience with Prefect (preferred) or Apache Airflow or Cloud Composer, BigQuery, and DataProc.

Cloud Infrastructure: Experienced in working with cloud platforms (GCP, AWS) and deploying data workloads and pipelines at scale.

Coding Agent: Demonstrated proficiency in using coding agents to accelerate the SDLC and plan and code complex engineering tasks.