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Vector Databases Jobs in Massachusetts (NOW HIRING)

Merck Group

Staff Software Engineer

Burlington, MA · On-site

$110K - $165K/yr

Vector Databases like Qdrant Nice to have Skills: * Experience with Kubernetes and container orchestration. * Familiarity with event-driven architectures and messaging platforms such as Kafka.

SevenAI

Senior AI Engineer

Boston, MA · On-site

$113K - $155K/yr

Tool fluency - comfortable with RAG, vector databases (e.g., Pinecone/Weaviate), workflow frameworks (LangChain, Dust), and related tooling. * Architectural thinker - you can diagram end-to-end ...

Chewy

Software Engineer III

Boston, MA · On-site +1

$136K - $225K/yr

Understanding of LLM architectures, embeddings, and vector databases (e.g., Qdrant, Pinecone, Milvus, FAISS). * Demonstrated ability to drive cross-team technical initiatives and influence ...

SolGenie

Full Stack UI Developer

Boston, MA · On-site

Experience working with LLMs and related AI frameworks (e.g., LangChain, Vector Databases, RAG). * Experience with API development and integration (RESTful APIs, GraphQL, cloud-based services)

SS&C

Senior Software Engineer

Waltham, MA

$132K - $174K/yr

Work with vector databases, embeddings, semantic search, and AI-driven APIs to build intelligent workflows and enhance product capabilities. * Support the development of AI-assisted features such as ...

SS&C Technologies

Senior Software Engineer

Waltham, MA · On-site

$132K - $174K/yr

Work with vector databases, embeddings, semantic search, and AI-driven APIs to build intelligent workflows and enhance product capabilities. * Support the development of AI-assisted features such as ...

Tempus

Data Architect, Next Platform

Boston, MA · On-site +1

$150K - $200K/yr

Experience with Vector databases (e.g., Pinecone, Weaviate, or pgvector) or Graph databases to support RAG and agentic memory. * Cloud Architecture: Hands-on experience with GCP (BigQuery, Vertex AI ...

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Showing results 21-40

All JobsVector Databases JobsVector Databases Jobs in Massachusetts

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

Staff Software Engineer

Merck Group

Burlington, MA • On-site

$110K - $165K/yr

Full-time

Medical, Retirement, PTO

Posted 12 days ago

Job description

Work Location: Burlington, Massachusetts
Shift:
Department: LS-DI-EN E2E Prod Eng Content & User and Operations
Recruiter: Anthony Johnson
This information is for internals only. Please do not share outside of the organization.
Your Role:
We are looking for a Staff Applied AI Engineer to build scalable, production-grade backend applications that leverage large and complex datasets. This role is software engineering first, with a strong emphasis on designing reliable systems that ingest, process, and serve data to power modern applications and AI-driven solutions.
You will work closely with product managers, software engineers, data scientists, and ML engineers to build robust backend services, data-intensive applications, and production AI systems. Success in this role requires strong software engineering fundamentals, practical experience working with data throughout its lifecycle, and the ability to design systems that are scalable, maintainable, and reliable in production.
Essential Job Functions:
  • Lead technical strategy for AI/LLM systems across multiple products
  • Architect retrieval, orchestration, agentic, and evaluation systems that run reliably in production
  • Set the standards for AI safety, evaluation, observability, and responsible rollout in a regulated context
  • Mentor Junior-level engineers into strong AI engineers; Employ AI Native development skills to multiply the productivity (Claude, etc.)
  • Lead the frontier: evaluate new models, techniques, and tools, and bring the right ones into the team
  • Design, develop, and maintain scalable Python applications and backend services.
  • Build systems that ingest, validate, transform, and manage structured and unstructured data in production environments.
  • Design data models and storage solutions that support scalable, high-performance applications.
  • Develop reusable components for data processing, validation, enrichment, and feature generation.

Who You Are
Minimum Qualifications:
  • Bachelor's degree in Computer Science, Engineering, Data Science, or a related quantitative field.
  • At least 3 years of hands-on experience in machine learning, data science, search relevance, or ranking systems.

Preferred Qualifications:
  • 10+ years of software engineering experience, with deep recent time leading production AI/LLM systems
  • Proven expertise in Python and ML frameworks (MLFlow, TensorFlow, PyTorch, Scikit- learn, or equivalent).
  • Strong background in statistical analysis, data exploration, and working with large-scale datasets.
  • Experience with feature engineering, data preprocessing, and data
  • Seasoned hands-on coder; still writes production Python regularly
  • Seasoned system designer for AI systems at scale - retrieval, agents, evaluation, latency, and cost, vector databases/pipelines
  • Strong experience building and maintaining production-grade backend applications.
  • Experience designing and developing RESTful APIs and distributed systems.
  • Strong SQL skills and experience working with relational databases; familiarity with NoSQL databases or modern data storage technologies is a plus.
  • Solid understanding of data engineering fundamentals, including data quality, validation, transformation, modeling, and efficient storage.
  • Experience designing systems that process large datasets reliably and efficiently.
  • Experience with cloud platforms such as Google Cloud Platform (GCP) or AWS.
  • Experience using Docker, Git, CI/CD pipelines, automated testing frameworks, and modern software engineering best practices.
  • Core engineering stack
  • Languages: Python, REST API, Pandas, NumPy
  • Cloud and infrastructure: AWS Services and/or GCP, Kubernetes, Bedrock
  • Distributed systems: event-driven architectures, including Kafka
  • Orchestration Frameworks: LangGraph, LangChain, AirFlow, etc.
  • Vector Databases like Qdrant

Nice to have Skills:
  • Experience with Kubernetes and container orchestration.
  • Familiarity with event-driven architectures and messaging platforms such as Kafka.
  • Familiarity with ML model deployment and inference pipelines

Location
This role can be based in either our St. Louis, MO or Burlington, MA office and requires a weekly onsite presence
Pay Range for this position: $110,500 - $165,900. The offer range represents the anticipated low and high end of the base pay compensation for this position. The actual compensation offered will be determined by factors such as location, level of experience, education, skills, and other job-related factors. Position may be eligible for sales or performance-based bonuses. Benefits offered by the Company include health insurance, paid time off (PTO), retirement contributions, and other perquisites. For more information click here: https://careers.emdgroup.com/us/en/benefits
The Company is an Equal Employment Opportunity employer. No employee or applicant for employment will be discriminated against on the basis of race, color, religion, age, sex, sexual orientation, national origin, ancestry, disability, military or veteran status, genetic information, gender identity, transgender status, marital status, or any other classification protected by applicable federal, state, or local law. This policy of Equal Employment Opportunity applies to all policies and programs relating to recruitment and hiring, promotion, compensation, benefits, discipline, termination, and all other terms and conditions of employment. Any applicant or employee who believes they have been discriminated against by the Company or anyone acting on behalf of the Company must report any concerns to their Human Resources Business Partner, Legal, or Compliance immediately. The Company will not retaliate against any individual because they made a good faith report of discrimination.

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