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Vector Databases Jobs in New York, NY (NOW HIRING)

GenAI Developer / Python

Manhattan, NY · On-site

$55.50 - $76.25/hr

PostgreSQL, Vector Databases, and Advanced Retrieval strategies. ML/DL: PyTorch, TensorFlow, and Model Fine-tuning. Deployment: Docker, Production API management, and LLM monitoring. Tools: Prompt ...

Be Seen First

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 ...

Lead Generative AI Developer

New York, NY · On-site

$176K - $265K/yr

Design and optimize data pipelines feeding AI systems, working with vector databases (e.g., Pinecone, Weaviate, pgvector) and enterprise data platforms. * Technical Leadership: Mentor junior ...

Python + Gen AI Developer - New York

Manhattan, NY · On-site

$55 - $76/hr

PostgreSQL, Vector Databases, and Advanced Retrieval strategies. ML/DL: PyTorch, TensorFlow, and Model Fine-tuning. Deployment: Docker, Production API management, and LLM monitoring. Tools: Prompt ...

PostgreSQL, Vector Databases, and Advanced Retrieval strategies. ML/DL: PyTorch, TensorFlow, and Model Fine-tuning. Deployment: Docker, Production API management, and LLM monitoring. Tools: Prompt ...

Develop RAG solutions using embeddings and vector databases. * Build and integrate REST APIs, microservices, and event-driven architectures. * Develop scalable backend applications using Python ...

Develop RAG solutions using embeddings and vector databases. * Build and integrate REST APIs, microservices, and event-driven architectures. * Develop scalable backend applications using Python ...

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 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 job categories do people searching Vector Databases jobs in New York, NY look for?

The top searched job categories for Vector Databases jobs in New York, NY are:

What cities near New York, NY are hiring for Vector Databases jobs?

Cities near New York, NY with the most Vector Databases job openings:

Infographic showing various Vector Databases job openings in New York, NY as of August 2026, with employment types broken down into 63% Full Time, and 37% Contract. Highlights an 100% In-person job distribution.

Senior Product Manager, AI Content Platform

Hearst

New York, NY • On-site

$138K - $182K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 25 days ago


Hearst rating

6.6

Company rating: 6.6 out of 10

Based on 28 frontline employees who took The Breakroom Quiz

57th of 78 rated media


Job description

About the Role

The Hearst Corporate Data Team is seeking a Senior Product Manager, AI Content Platform to lead the strategy, development, and evolution of Hearst's enterprise AI content platform. Reporting to the VP, Identity Product Development, this role will own the product vision and execution for the foundational capabilities that enable AI-powered search, personalization, recommendation, knowledge retrieval, and generative AI applications across Hearst.

You will define and deliver the next generation of enterprise content intelligence services, including semantic enrichment, vector embeddings, knowledge graphs, content ontology, metadata, and semantic search capabilities. Working closely with Product, Engineering, and business stakeholders, you will transform Hearst's content into structured, machine-understandable assets that power enterprise AI products and experiences.

The ideal candidate combines deep product management experience with a strong understanding of AI, semantic technologies, and modern data platforms. This role is hybrid and based in New York City.


Key Responsibilities
  • Own the product strategy, roadmap, and execution for Hearst's enterprise AI Content Platform, serving as the foundation for AI-powered products and services across the company.

  • Lead development of enterprise content intelligence capabilities including vector embeddings, semantic search, knowledge graphs, ontology management, metadata enrichment, entity extraction, and AI-driven content understanding.
  • Define the product vision for scalable content processing pipelines that transform editorial content into AI-ready assets supporting retrieval, recommendation, personalization, and generative AI applications.
  • Partner closely with Engineering, Product, and business stakeholders to prioritize platform capabilities and deliver enterprise solutions.
  • Drive development of semantic search, vector databases, retrieval pipelines, and RAG capabilities that improve discoverability and AI performance.
  • Evaluate emerging AI technologies, foundation models, embedding models, vector databases, and semantic technologies to continuously improve platform capabilities.
  • Translate complex AI and machine learning concepts into product requirements and actionable engineering roadmaps.
  • Serve as the product leader for enterprise content intelligence, enabling downstream personalization, advertising, audience insights, and AI-powered consumer experiences.


Qualifications
  • 7-10 years of experience in Product Management or Platform Product Management, with experience building enterprise platforms or AI-powered products.

  • Strong understanding of modern AI technologies, including LLMs, RAG, vector embeddings, semantic search and knowledge graphs.
  • Experience working with vector databases, search technologies, metadata management, content processing pipelines, or knowledge management platforms.
  • Experience defining product strategy, roadmaps, requirements, and success metrics for highly technical platforms.
  • Strong analytical and systems-thinking skills with the ability to translate emerging technologies into scalable enterprise capabilities.
  • Excellent communication skills with experience influencing senior leadership and aligning cross-functional stakeholders around long-term platform strategy.
  • Bachelor's degree required; advanced degree in Computer Science, Data Science, Engineering, Information Science, or related field preferred.
  • Preferred experience in digital media, publishing, advertising technology, recommendation systems, search technologies, or enterprise AI platforms.

Compensation & Benefits

In accordance with applicable law, Hearst is required to include a reasonable estimate of the compensation for this role if hired in New York City. The reasonable estimate, if hired in New York City, is $175,000-$195,000. Please note this information is specific to those hired in New York City. For candidates outside New York City, the salary range will be aligned with the specific location. A final decision on the successful candidate's starting salary will be based on a number of permissible, non-discriminatory factors, including but not limited to skills, experience, training, certifications, and education.  Hearst provides a competitive benefits package, including medical, dental, vision, disability, and life insurance, 401(k), paid holidays and paid time off, employee assistance programs, and more.


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