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Vector Databases Jobs in Redmond, WA (NOW HIRING)

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 ...

AI Engineer Intern

Bellevue, WA · On-site

$82.66 - $165.31/hr

Create embeddings pipelines and manage vector databases for semantic search * Monitor AI system performance, costs, and quality metrics in production Requirements * Strong Python skills with async ...

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 ...

... 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 ...

Founded by the engineers behind Milvus, the world's most popular open-source vector database, the company builds next-generation database technologies to help organizations quickly create AI ...

Deploy, configure, and maintain vector database infrastructure for embedding storage, indexing, and semantic retrieval at scale. * Design and maintain scalable graph query APIs consumed by internal ...

Deploy, configure, and maintain vector database infrastructure for embedding storage, indexing, and semantic retrieval at scale. * Design and maintain scalable graph query APIs consumed by internal ...

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 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 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 Redmond, WA?

For Vector Databases jobs in Redmond, WA, the most frequently searched job titles are:

What job categories do people searching Vector Databases jobs in Redmond, WA look for?

The top searched job categories for Vector Databases jobs in Redmond, WA are:

What cities near Redmond, WA are hiring for Vector Databases jobs?

Cities near Redmond, WA with the most Vector Databases job openings:

Infographic showing various Vector Databases job openings in Redmond, WA as of August 2026, with employment types broken down into 87% Full Time, 8% Part Time, and 5% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution.

Principal Software Engineer - Vector Search - Elasticsearch

Elasticsearch B.V.

Seattle, WA • On-site

$159.80 - $252.80/hr

Other

Medical, PTO

Posted 8 days ago


Job description

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI.

What is The Role:

We're looking for a Principal Software Engineer to join the Elasticsearch - Search team. This globally-distributed team of expert engineers focuses on delivering a robust and feature-rich search experience, including contributing to improving the search experience in Lucene. This is a principal software engineering role that focuses on enhancing the vector similarity search functionality within Elasticsearch, covering the design and implementation of new vector search features, enhancements to existing vector search functionality, and resolving bugs.

Our company is distributed by intention. We hire the best engineers we can find wherever they are, whoever they are. We collaborate across continents every day over email, GitHub, Zoom, and Slack. At our best, we write fast, scalable and intuitive software. We believe that the best way to do that is to empower individual engineers, code review every change, decide big things by consensus, and strive for incremental improvements.

What You Will Be Doing:
  • Lead initiatives within Elasticsearch to produce an industry-leading vector database offering, supplying unparalleled speed and relevance in search.
  • Contribute to Elasticsearch full time, building new search features and fixing intriguing bugs, all while making the code easier to understand. Sometimes you'll need to invent a new algorithm or data structure. Or find one and implement it. Sometimes you'll need to get close to the operating system and hardware.
  • Work with a globally distributed team of experienced engineers focused on the vector search capabilities of Elasticsearch.
  • Be an expert on how Elasticsearch implements vector similarity in support of search relevance and everyone will turn to you when they have a question about this area. You'll improve this area based on your questions and your instincts.
  • Work with community members from all over the world on issues and pull requests, sometimes triaging them and handing them off to other experts and sometimes handling them yourself.
  • Write idiomatic modern Java -- Elasticsearch is 99.8% Java!
What You Bring:
  • You have implemented novel techniques in vector similarity on a search platform with a large user base or progressed the field of academic research in vector similarity information retrieval.
  • Professional experience with vector similarity and vector databases, and you used HNSW, IVF, or other relevant algorithms and libraries on search platforms at scale.
  • You have strong skills in core Java and are conversant in the standard library of data structures and concurrency constructs, as well as other features like lambdas.
  • You work with a high level of autonomy, and are able to take on projects and guide them from beginning to end. This covers both technical design and working with other engineers to develop needed components.
  • You’re comfortable developing collaboratively. Giving and receiving feedback on code and approaches and APIs is hard! Bonus points if you’ve collaborated over the internet because that’s harder. Double bonus points for asynchronous collaboration over the internet. That’s even harder, but we do it anyway because it’s the best way we know how to build software.
  • You’ve used several data storage technologies like Elasticsearch, Solr, PostgreSQL, MongoDB, or Cassandra and have some idea how they work and why they work that way.
  • You have excellent verbal and written communication skills. Like we said, collaborating on the internet is hard. We try to be respectful, empathetic, and trusting in all of our interactions. And we’d expect that from you too.
Bonus Points:
  • You've built things with Elasticsearch before.
  • You've worked with open source projects and are familiar with different styles of source control workflow and continuous integration.
  • Experience with data storage technology.
  • You have experience designing, leading and owning cross-functional initiatives
Additional Information - We Take Care of Our People:

As a distributed company, diversity drives our identity. Whether you’re looking to launch a new career or grow an existing one, Elastic is the type of company where you can balance great work with great life. Your age is only a number. It doesn’t matter if you’re just out of college or your children are; we need you for what you can do.

We strive to have parity of benefits across regions, and while regulations differ from place to place, we believe taking care of our people is the right thing to do.

  • Competitive pay based on the work you do here and not your previous salary
  • Health coverage for you and your family in many locations
  • Ability to craft your calendar with flexible locations and schedules for many roles
  • Generous number of vacation days each year
  • Increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service
  • Up to 40 hours each year to use toward volunteer projects you love
  • Embracing parenthood with a minimum of 16 weeks of parental leave

Security & Privacy Responsibilities: Take ownership of protecting the confidentiality, integrity, and availability of organizational data and systems by following applicable privacy and security policies, standards, and procedures. Ensure that all individual contributions follow Elastic’s Secure Software Development Framework (SSDF). Proactively participate in mandatory role-based training to ensure personal technical execution consistently aligns with the highest standards of data protection, data privacy, and system resilience.

Different people approach problems differently. We need that. Elastic is an equal opportunity employer and is committed to creating an inclusive culture that celebrates different perspectives, experiences, and backgrounds. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, pregnancy, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, disability status, or any other basis protected by federal, state or local law, ordinance or regulation.

We welcome individuals with disabilities and strive to create an accessible and inclusive experience for all individuals. To request an accommodation during the application or the recruiting process, please email candidate_accessibility@elastic.co. We will reply to your request within 24 business hours of submission.

Applicants have rights under Federal Employment Laws and can view the following posters linked below:

Family and Medical Leave Act (FMLA) Poster

Employee Polygraph Protection Act (EPPA) Poster

Elasticsearch develops and distributes technology and information that is subject to U.S. and other countries’ export controls and licensing requirements for individuals who are located in or are nationals of the following sanctioned countries and regions: Belarus, Cuba, Iran, North Korea, Syria, or Russia, including the Ukrainian territories annexed by Russia (The Crimea region of Ukraine, The Donetsk People’s Republic (DNR), The Luhansk People’s Republic (LNR), Kherson or Zaporizhzhia). If you are located in or are a national of one of the listed countries or regions, an export license may be required as a condition of your employment in this role. Please note that national origin and/or nationality do not affect eligibility for employment with Elastic.

Please see here for our Privacy Statement.

Compensation for this role is in the form of base salary. This role does not have a variable compensation component.

The typical starting salary range for this role is:

$159,800—$252,800 USD

The typical starting salary range for this role in the select locations listed above is:

$191,900—$303,500 USD

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