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Knowledge Graph Engineer Jobs in Massachusetts (NOW HIRING)

Architect Data Engineer - AI Platforms Experience Level: 10+ yrs Work Location: US East/Canada ... Define multi-tenant schemas and Knowledge Graph ontologies that allow LLM agents to perform complex ...

Architect Data Engineer - AI Platforms Experience Level:10+ yrs Work Location:US East/Canada ... Define multi-tenant schemas and Knowledge Graph ontologies that allow LLM agents to perform complex ...

Be a key contributor to the design and implementation of a scalable knowledge graph infrastructure ... Programming background in parser combinators, natural language processing, and linked data (RDF ...

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Knowledge Graph Engineer information

Which IT job is the highest paid?

In the IT industry, roles such as Chief Information Officer (CIO), Solutions Architect, and Cloud Engineer tend to be among the highest paid, often earning six-figure salaries. Specialized skills in cybersecurity, cloud computing, and data management can also command top compensation levels for experienced professionals.

What engineers make 500,000?

Senior-level engineers in specialized fields such as software engineering, data engineering, or machine learning engineering can earn $500,000 or more annually, especially with extensive experience, advanced skills, and in high-demand industries. Compensation often includes base salary, bonuses, and stock options, particularly at large tech companies or startups with significant funding.

What engineers make 300,000 a year?

Senior engineers in specialized fields such as software engineering, data engineering, and machine learning engineering can earn $300,000 or more annually, especially with extensive experience, advanced skills, and working in high-demand industries or companies. Roles often require strong technical expertise, certifications, and sometimes leadership responsibilities.

Are knowledge graphs the future?

Knowledge Graph Engineers work with structured data models that represent relationships between entities, and knowledge graphs are increasingly used in AI, search engines, and data integration. As organizations seek to improve data understanding and interoperability, expertise in knowledge graphs is expected to remain in demand, especially with skills in graph databases and semantic modeling. This trend suggests that knowledge graphs are likely to play a significant role in future data-driven applications.
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Infographic showing various Knowledge Graph Engineer job openings in Massachusetts as of June 2026, with employment types broken down into 60% Full Time, and 40% Part Time. Highlights an 60% In-person, and 40% Remote job distribution.

Director, Knowledge Graph & Semantics - HYBRID ROLE

Vrtx

Boston, MA • Hybrid

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 25 days ago


Job description

Job Description

This is a Hybrid position requiring 3 days a week in our Boston office

We are seeking an experienced engineering leader to build and operate Vertex's enterprise Knowledge Graph and Semantic Layer: the unified, navigable representation of Vertex's data, concepts, and relationships that AI agents and analytical systems traverse to reason over our business. Vertex Pharmaceuticals is in a transformational period, and the AI team is at the center of our AI-first strategy, delivering AI solutions that empower executives, researchers, and business users to make faster, more confident decisions.

As part of the Vertex Data Engineering team, you will lead the Knowledge Graph & Semantics function within Knowledge & Grounding. Your team builds the graph that connects Vertex across clinical, research, regulatory, and commercial domains, and the AI semantic layer that defines the business meaning, metrics, and dimensions that sit on top of it. The capability you build becomes the substrate that AI agents traverse to ground their reasoning, and that analytical systems use to answer cross-domain questions consistently across the enterprise.

As the Director of Knowledge Graph & Semantics, you will define the graph and semantic strategy for Vertex, select the underlying technology stack, and lead the engineering team that brings it to life.

The capability you build will accelerate every downstream AI and analytics initiative at Vertex by giving them a single, governed, traversable model of how Vertex's data fits together.

Key Duties and Responsibilities:

  • Enterprise Knowledge Graph. Design, build, and operate Vertex's enterprise knowledge graph spanning clinical, research, regulatory, and commercial domains, including ingestion, storage, query, and lifecycle management of nodes, edges, and properties.

  • Semantic Layer. Build and govern the enterprise semantic layer that enables metrics, dimensions, business entities, and relationships in a single, consistent model used by AI agents.

  • Graph and Semantic Strategy. Define Vertex's strategy for graph and semantic platform, including technology selection (graph database, AI semantic layer tooling, query API) and the architecture that unifies them.

  • Ontology Partnership. Partner with the ontology and data modeling function to translate domain ontologies into the graph, ensuring fidelity to source models and consistency across domains.

  • Agent Traversal & Retrieval. Build the graph traversal and retrieval interfaces that AI agents and other consumers use to ground their reasoning, including pattern queries, semantic search over graph context, and graph-aware retrieval for RAG systems.

  • Application & System Onboarding. Partner with application and system owners across Vertex to onboard their systems into the enterprise knowledge graph and semantic layer.

  • Production Operations. Own SLAs, observability, query performance, cost, and continuous improvement for the graph and semantic layer in production.

Knowledge and Skills:

  • Proven Experience. 10+ years of experience in data engineering, AI/ML, or advanced analytics, with 3+ years specifically focused on knowledge graphs, semantic technologies, or enterprise data modeling at scale.

  • Knowledge Graph Expertise. Deep hands-on experience designing and operating enterprise knowledge graphs, including schema design, ingestion, query, and traversal patterns. Familiarity with multiple graph paradigms (property graph, RDF/semantic web, hybrid graph + vector approaches) and the trade-offs between them.

  • Semantic Layer Expertise. Strong experience building and governing semantic layers (e.g., dbt Semantic Layer, Cube, AtScale, LookML, or comparable) that serve analytics and AI consumers consistently.

  • Cloud Data Platforms. Strong experience with Snowflake and/or Databricks in enterprise environments, including how graph and semantic capabilities integrate with these platforms.

  • Cross-Domain Data Integration. Track record of integrating data across multiple business domains, including entity resolution, master data, and lineage at enterprise scale.

  • AI & Agent Grounding. Working understanding of how knowledge graphs and semantic layers are consumed by AI agents and RAG systems, including graph-aware retrieval and traversal for agent reasoning.

  • Production Operations. Track record of operating graph and analytical systems in production with high availability, query performance, and continuous improvement.

  • Leadership & Communication. Proven ability to lead technical teams, communicate with executive stakeholders, and translate business needs into graph and semantic models.

Preferred

  • Pharma or Life Sciences Context. Experience in pharmaceutical, biotech, or life sciences data environments. Familiarity with clinical, regulatory, and commercial data domains is a plus.

  • Regulated Environment Experience. Working knowledge of GxP, 21 CFR Part 11, and validated-system constraints on data and AI deployments.

  • Life Sciences Ontologies. Familiarity with industry ontologies and standards (e.g., SNOMED CT, MedDRA, LOINC, RxNorm, CDISC, IDMP) and how they map into enterprise graph models.

  • Graph Query Languages. Hands-on experience with Cypher, SPARQL, Gremlin, GQL, or comparable graph query languages.

  • LLM Integration with Graphs. Experience with text-to-Cypher, text-to-SPARQL, or other LLM-driven graph query generation, and graph-augmented retrieval for AI systems.

    Pay Range:

    $216,400 - $324,600

    Disclosure Statement:

    The range provided is based on what we believe is a reasonable estimate for the base salary pay range for this job at the time of posting. This role is eligible for an annual bonus and annual equity awards. Some roles may also be eligible for overtime pay, in accordance with federal and state requirements. Actual base salary pay will be based on a number of factors, including skills, competencies, experience, and other job-related factors permitted by law.

    At Vertex, our Total Rewards offerings also include inclusive market-leading benefits to meet our employees wherever they are in their career, financial, family and wellbeing journey while providing flexibility and resources to support their growth and aspirations. From medical, dental and vision benefits to generous paid time off (including a week-long company shutdown in the Summer and the Winter), educational assistance programs including student loan repayment, a generous commuting subsidy, matching charitable donations, 401(k) and so much more.

    Flex Designation:

    Remote-Eligible

    Flex Eligibility Status:

    In this Remote-Eligible role, you can choose to be designated as:
    1. Remote: work remotely five days per week and come into the office on occasion - you're always welcome on-site; or select
    2. Hybrid: work remotely up to two days per week; or select
    3. On-Site: work five days per week on-site with ad hoc flexibility.

    Note: The Flex status for this position is subject to Vertex's Policy on Flex @ Vertex Program and may be changed at any time.

    #LI-Remote

    Company Information

    Vertex is a global biotechnology company that invests in scientific innovation.

    Vertex is committed to equal employment opportunity and non-discrimination for all employees and qualified applicants without regard to a person's race, color, sex, gender identity or expression, age, religion, national origin, ancestry, ethnicity, disability, veteran status, genetic information, sexual orientation, marital status, or any characteristic protected under applicable law. Vertex is an E-Verify Employer in the United States. Vertex will make reasonable accommodations for qualified individuals with known disabilities, in accordance with applicable law.

    Any applicant requiring an accommodation in connection with the hiring process and/or to perform the essential functions of the position for which the applicant has applied should make a request to the recruiter or hiring manager, or contact Talent Acquisition at ApplicationAssistance@vrtx.com