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Vector Databases Jobs in Queens, NY (NOW HIRING)

VAST Data

Senior Software Engineer - Database

Manhattan, NY · On-site +1

$116K - $158K/yr

Proven track record of backend work on high-throughput databases, vector stores, or real-time processing engines * Bachelor's degree in Computer Science, Engineering, or equivalent experience Nice to ...

VAST Data

Senior Software Engineer - Database

Manhattan, NY · On-site +1

$116K - $158K/yr

Proven track record of backend work on high-throughput databases, vector stores, or real-time processing engines * Bachelor's degree in Computer Science, Engineering, or equivalent experience Nice to ...

3B Staffing LLC

Data Analytics Engineer (AI)

Stamford, CT

$122K - $146K/yr

Develop and manage prompt engineering frameworks, embeddings, and vector databases * Translate business requirements into AI-powered workflows and intelligent automation solutions AI Implementation ...

Merican

Context Engineer (Model Risk Focus)

New York, NY · On-site

$50 - $53/hr

Work with vector databases and embedding techniques to enable retrieval augmented context. * Partner with AI engineers and architects to integrate context layers into agentic systems. Required Skills

Merican

AI Architect - Model Lifecycle

New York, NY · On-site

$57 - $60/hr

Design scalable architecture using vector databases, embeddings, and workflow orchestration. * Work with engineering teams to integrate AI components into enterprise systems. * Provide technical ...

BIGDATAAPPTECH LLC

Gen AI Architect

Jersey City, NJ · On-site

RAG Pipelines, Vector Databases & MCP Architect and deploy RAG pipelines using vector databases such as: Pinecone Weaviate ChromaDB FAISS Implement MCP Servers and Agent-to-Agent (A2A) communication ...

Photon

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 ...

Photon

AI Engineer - NY

Manhattan, NY · On-site

Integrate agents with vector databases, RAG pipelines, and knowledge graphs. Production AI Systems * Implement observability, evaluation, and guardrails for agent behavior. * Optimize AI pipelines ...

Accord Technologies Inc.

Senior AI Engineer

Piscataway, NJ · On-site

$106K - $146K/yr

Design and implement solutions involving Large Language Models (LLMs), embeddings, vector databases, Retrieval-Augmented Generation (RAG), and prompt engineering. * Work with cloud AI services such ...

VRN Technologies

AI Native Engineer

Jersey City, NJ · On-site

Develop Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search. * API/SDK Integration: Integrate LLMs (OpenAI, Anthropic) into applications using function calling ...

Drunix Solution Inc

AWS Developer

Newark, NJ · On-site

Develop Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search. * API/SDK Integration: Integrate LLMs (OpenAI, Anthropic) into applications using function calling ...

New

Technodeed LLC

AI Platform Engineer

Manhattan, NY · On-site

GPT, Claude, Gemini, LLaMA ✔ Agentic AI, MCP, Graph/RAG, LLM Pipelines ✔ IAM, Security Controls, Logging & Compliance ✔ Terraform, Docker, Infrastructure as Code ✔ Vector Databases (PGVector ...

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All JobsVector Databases JobsVector Databases Jobs in Queens, NY

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 Queens, NY? For Vector Databases jobs in Queens, NY, the most frequently searched job titles are:
What job categories do people searching Vector Databases jobs in Queens, NY look for? The top searched job categories for Vector Databases jobs in Queens, NY are:
What cities near Queens, NY are hiring for Vector Databases jobs? Cities near Queens, NY with the most Vector Databases job openings:
Vector Databases jobs near you

Senior Software Engineer - Database

VAST Data

Manhattan, NY • On-site, Remote

$116K - $158K/yr

Full-time

Re-posted 27 days ago

Job description

Description
VAST Data is looking for a Senior Software Engineer to join our growing team!
This is a great opportunity to join one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence. Take part in the design and implementation of the internals of the next-generation hugely scalable and highly performant analytical and vector database.
"VAST's data management vision is the future of the market."- Forbes
VAST Data is the data platform company for the AI era. We are building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference. Designed from the ground up to make AI simple to deploy and manage, VAST takes the cost and complexity out of deploying enterprise and AI infrastructure across data center, edge, and cloud.
Our success has been built through intense innovation, a customer-first mentality and a team of fearless VASTronauts who leverage their skills & experiences to make real market impact. This is an opportunity to be a key contributor at a pivotal time in our company's growth and at a pivotal point in computing history.
VAST Data is looking for a Senior Backend Software Engineer to help build the engine behind the next generation of scalable, AI-native data infrastructure. In this role, you will focus on the design and development of backend services powering our massively distributed, high-performance combined analytical and vector database, a critical component of VAST's AI data platform.
This is your opportunity to work at the intersection of low-level systems programming, distributed computing, and AI infrastructure-helping us push the boundaries of backend engineering for real-time, petabyte-scale data systems.
What You'll Do
  • Architect and implement core backend components for a distributed vector database using C/C++
  • Design highly scalable distributed data-structures and algorithms optimized for performance, concurrency, and fault tolerance
  • Develop backend services that enable fast search, efficient indexing, and real-time analytics over massive datasets
  • Optimize system performance across multi-threaded and multi-node environments
  • Ensure low-latency, high-throughput data access and manipulation across global deployments
  • Collaborate closely with cross-functional teams to translate backend capabilities into real-world impact

Requirements
What We're Looking For
Must Haves:
  • 5+ years of experience in backend engineering, with strong proficiency in low-level C and C++
  • Hands-on experience designing and building distributed backend systems or infrastructure at scale
  • Experience with distributed data-structures, algorithms and system reliability patterns
  • Expertise in multi-threaded programming, memory management, and performance tuning
  • Proven track record of backend work on high-throughput databases, vector stores, or real-time processing engines
  • Bachelor's degree in Computer Science, Engineering, or equivalent experience

Nice to Haves:
  • Experience building or optimizing analytical or vector databases
  • Familiarity with query engine internals, indexing techniques, or storage layer optimizations
  • Knowledge of Python or Java for integration or tooling
  • Bachelor's, Master's or PhD in a related technical field (distributed systems, backend architecture, database internals)

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