1

Knowledge Engineering Jobs in New York (NOW HIRING)

Principal Knowledge Engineer

Manhattan, NY · On-site

$197.30 - $313.70/hr

Establish enterprise standards, governance models, engineering patterns, and best practices for Knowledge Graph development, deployment, and lifecycle management.* Define strategies for integrating ...

As our first GTM Knowledge Engineer, you'll sit at the intersection of Revenue Enablement, Revenue ... Custom GTM Agent Engineering: Design, code, and ship localized context-aware GTM Agents that solve ...

As our first GTM Knowledge Engineer, you'll sit at the intersection of Revenue Enablement, Revenue ... Custom GTM Agent Engineering: Design, code, and ship localized context-aware GTM Agents that solve ...

As our first GTM Knowledge Engineer, you'll sit at the intersection of Revenue Enablement, Revenue ... Custom GTM Agent Engineering: Design, code, and ship localized context-aware GTM Agents that solve ...

This job is part of the Engineering and Technical Services job function. They are responsible for ... Apply in-depth knowledge of standard principles and techniques/procedures to accomplish complex ...

This job is part of the Engineering and Technical Services job function. They are responsible for ... Apply in-depth knowledge of standard principles and techniques/procedures to accomplish complex ...

This position is part of CBRE's Engineering & Technical Services function, where our professionals ... Apply in-depth knowledge of standard principles and techniques/procedures to accomplish complex ...

Collaborate with product, engineering, and delivery teams to translate customer requirements into ... In-depth knowledge of semantic web standards (RDF, SKOS, OWL, SPARQL) * Familiarity with graph ...

Collaborate with product, engineering, and delivery teams to translate customer requirements into ... In-depth knowledge of semantic web standards (RDF, SKOS, OWL, SPARQL) * Familiarity with graph ...

next page

Showing results 1-20

Knowledge Engineering information

What is knowledge engineering?

Knowledge engineering is a field within artificial intelligence that focuses on creating systems capable of simulating human decision-making and reasoning. It involves gathering, organizing, and structuring information so that computers can use it to solve complex problems. Knowledge engineers work to build knowledge bases and rule-based systems, often collaborating with domain experts to codify expertise into a form that machines can process. This discipline is fundamental in the development of expert systems, intelligent agents, and modern AI applications.

What are the key skills and qualifications needed to thrive as a knowledge engineer?

To thrive as a Knowledge Engineer, you need a strong background in computer science, logic, and data modeling, often supported by a relevant degree. Familiarity with knowledge representation systems, ontologies, semantic web technologies, and tools like Protégé is typically required, along with experience in programming languages such as Python or Java. Strong analytical thinking, problem-solving abilities, and clear communication skills help you collaborate with subject matter experts and translate complex information into structured formats. These skills are critical for building effective knowledge-based systems that drive intelligent decision-making and organizational efficiency.

How does a knowledge engineer typically collaborate with subject matter experts during a project?

Knowledge Engineers frequently work closely with subject matter experts (SMEs) to extract, structure, and formalize domain knowledge into usable formats for AI systems or knowledge bases. This collaboration often involves conducting interviews, facilitating workshops, and reviewing documentation to ensure complex concepts are accurately captured. Effective communication and iterative feedback are key, as Knowledge Engineers must bridge the gap between technical requirements and expert insights. This teamwork helps ensure that the resulting system is both technically sound and aligned with real-world practices.

What is the difference between Knowledge Engineering vs Data Scientist?

AspectKnowledge EngineeringData Scientist
Required CredentialsTypically degrees in computer science, AI, or related fields; certifications in knowledge systemsDegrees in statistics, computer science, or mathematics; certifications in data analysis or machine learning
Work EnvironmentDeveloping knowledge bases, expert systems, and AI applications in tech or research settingsAnalyzing data, building predictive models, and deriving insights in various industries
Employer & Industry UsageUsed in AI development, research institutions, and tech companiesUsed across finance, healthcare, marketing, and tech sectors

While both roles involve working with data and AI, Knowledge Engineers focus on creating structured knowledge bases and expert systems, whereas Data Scientists analyze data to extract insights and build predictive models. Understanding these differences helps in choosing the right career path or job focus.

How much does a knowledge engineer make?

The average salary for a knowledge engineer typically ranges from $80,000 to $130,000 annually, depending on experience, education, and location. Knowledge engineers often work with AI, machine learning, and data management tools, and advanced skills can lead to higher compensation.

How to become a knowledge engineer?

To become a knowledge engineer, typically a bachelor's degree in computer science, information systems, or a related field is required, along with skills in knowledge representation, logic, and programming languages such as Python or Java. Experience with artificial intelligence, machine learning, and knowledge management tools is also valuable, and some roles may prefer candidates with advanced degrees or certifications in relevant areas.

What does a knowledge engineer do?

A knowledge engineer designs, develops, and maintains systems that capture and organize knowledge for artificial intelligence and expert systems. They analyze information, create ontologies, and use tools like knowledge bases and reasoning algorithms to enable machines to simulate human decision-making. Strong skills in logic, data modeling, and programming are essential for this role.

What are popular job titles related to Knowledge Engineering jobs in New York?

For Knowledge Engineering jobs in New York, the most frequently searched job titles are:

What job categories do people searching Knowledge Engineering jobs in New York look for?

The top searched job categories for Knowledge Engineering jobs in New York are:

What cities in New York are hiring for Knowledge Engineering jobs?

Cities in New York with the most Knowledge Engineering job openings:

Infographic showing various Knowledge Engineering job openings in New York as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 19% Part Time, and 2% Contract. Highlights an 90% Physical, 2% Hybrid, and 8% Remote job distribution.

Principal Scientist, Data Science (Translational Knowledge Engineering)

Johnson & Johnson

Raritan, NJ

Full-time

Retirement, PTO

Re-posted 4 days ago


Johnson & Johnson rating

8.3

Company rating: 8.3 out of 10

Based on 112 frontline employees who took The Breakroom Quiz

25th of 86 rated pharmaceutical


Job description

At Johnson & Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where treatments are smarter and less invasive, andsolutions are personal.Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.Learn more at jnj.com

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function:

Data Analytics & Computational Sciences

Job Sub Function:

Data Science

Job Category:

Scientific/Technology

All Job Posting Locations:

Cambridge, Massachusetts, United States of America, Horsham, Pennsylvania, United States of America, Raritan, New Jersey, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

Job Description:

About Innovative Medicine
Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.
Learn more at https://www.jnj.com/innovative-medicine

Position Summary

The Principal Translational Knowledge Architect & Graph Lead will be responsible for designing and implementing the semantic and knowledge architecture that enables AI-driven reasoning across the drug discovery and development lifecycle.

This role will serve as the scientific and technical lead for ontology development, knowledge graph design, semantic interoperability, and AI-ready knowledge representation. Working at the intersection of translational science, patient safety, biomedical informatics, and artificial intelligence, this individual will help establish the semantic foundation required to connect discovery biology, preclinical safety, clinical development, real-world evidence, and post-marketing safety into a unified reasoning framework.

The successful candidate will partner closely with scientists, safety experts, data scientists, AI engineers, and platform teams to create knowledge assets that support GraphRAG, agentic AI, scientific reasoning, and next-generation translational intelligence capabilities.

Mission

Build the semantic foundation that enables AI systems to reason across discovery, preclinical, clinical, and post-marketing domains while preserving scientific meaning, provenance, and translational fidelity.

Key Responsibilities

Semantic Architecture & Knowledge Modeling

  • Design and maintain enterprise knowledge models spanning:

    • Discovery biology

    • Toxicology

    • Safety pharmacology

    • Pathology

    • Clinical development

    • Pharmacovigilance

    • Real-world evidence

  • Develop semantic frameworks that support translational reasoning across the R&D lifecycle.

  • Create conceptual, logical, and physical knowledge models supporting AI-enabled scientific discovery.

Ontology Engineering & Governance

  • Lead ontology strategy, development, governance, and lifecycle management.

  • Curate and extend biomedical ontologies supporting translational safety and efficacy use cases.

  • Establish ontology governance processes, quality standards, and semantic review procedures.

  • Ensure semantic consistency, provenance, traceability, and FAIR data principles.

Knowledge Graph & Reasoning Infrastructure

  • Design RDF-based knowledge graph architectures and related semantic technologies.

  • Develop semantic mappings, inference rules, and reasoning frameworks supporting scientific decision-making.

  • Define knowledge representations enabling GraphRAG, semantic retrieval, AI agents, and reasoning systems.

  • Establish semantic interoperability across heterogeneous data sources and standards.

Translational Data Harmonization

  • Develop semantic bridges across major industry standards and ontologies, including:

    • SEND

    • SDTM

    • ADaM

    • MedDRA

    • HPO

    • MONDO

    • SNOMED CT

    • FHIR

    • OMOP

    • Cell Ontology

    • Protein Ontology

  • Enable AI systems to traverse translational boundaries while preserving biological and clinical context.

Scientific & Cross-Functional Leadership

  • Partner with stakeholders across Discovery, Preclinical Safety, Clinical Development, Pharmacovigilance, Data Science, and Digital Health.

  • Collaborate with engineering teams responsible for data products, pipelines, and AI platforms.

  • Influence enterprise semantic strategy and represent the organization in external standards and ontology communities when appropriate.

Required Qualifications

Education

  • PhD or Master's degree in:

    • Biomedical Informatics

    • Bioinformatics

    • Computational Biology

    • Computer Science

    • Information Science

    • Knowledge Engineering

    • Related scientific discipline

Experience

  • 5+ years of experience in biomedical informatics, semantic technologies, knowledge engineering, or scientific data architecture.

  • Demonstrated experience designing ontology-driven knowledge systems in life sciences, healthcare, or pharmaceutical R&D environments.

  • Experience working across multiple phases of drug discovery and development.

Technical Expertise

Deep expertise in:

  • Ontology development and governance

  • Knowledge representation

  • RDF

  • OWL

  • SHACL

  • SPARQL

  • Semantic Web technologies

Strong experience with:

  • Enterprise ontology management platforms

  • RDF graph architectures

  • Semantic APIs

  • FAIR data principles

Domain Knowledge

Strong familiarity with one or more of:

  • Translational science

  • Toxicology

  • Safety pharmacology

  • Clinical development

  • Pharmacovigilance

  • Regulatory data standards

Experience working with:

  • SEND

  • SDTM

  • ADaM

  • MedDRA

  • HPO

  • MONDO

  • FHIR

  • OMOP

Preferred Qualifications

  • Experience building semantic foundations for AI, GraphRAG, agentic AI, or scientific reasoning systems.

  • Familiarity with LLM-based retrieval and reasoning architectures.

  • Experience supporting translational safety, efficacy, biomarker, or mechanistic reasoning use cases.

  • Contributions to ontology standards, open-source biomedical ontologies, or scientific knowledge graph initiatives.

Leadership Competencies

  • Strategic thinker capable of translating scientific challenges into scalable knowledge architectures.

  • Strong communicator who can engage effectively with scientists, clinicians, data scientists, engineers, and senior leadership.

  • Ability to operate in ambiguous, highly cross-functional environments.

  • Passion for advancing AI-enabled drug discovery and development through semantic and knowledge-driven approaches.

Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.


Johnson & Johnson is committed to providing an interview process that is inclusive of our applicants' needs. If you are an individual with a disability and would like to request an accommodation, external applicants please contact us via https://www.jnj.com/contact-us/careers , internal employees contact AskGS to be directed to your accommodation resource.
#LI-GR1
#LI-Hybrid
#JRDDS
#JNJDataScience
#JRD

Required Skills:

Preferred Skills:

Advanced Analytics, Coaching, Critical Thinking, Data Analysis, Data Privacy Standards, Data Quality, Data Reporting, Data Savvy, Data Science, Data Visualization, Digital Fluency, Econometric Models, Organizing, Process Improvements, Strategic Thinking, Technical Credibility, Workflow Analysis

The anticipated base pay range for this position is :

$117,000.00 - $201,250.00

Additional Description for Pay Transparency:

Subject to the terms of their respective plans, employees are eligible to participate in the Company's consolidated retirement plan (pension) and savings plan (401(k)).
This position is eligible to participate in the Company's long-term incentive program.
Subject to the terms of their respective policies and date of hire, employees are eligible for the following time off benefits:
Vacation -120 hours per calendar year
Sick time - 40 hours per calendar year; for employees who reside in the State of Colorado -48 hours per calendar year; for employees who reside in the State of Washington -56 hours per calendar year
Holiday pay, including Floating Holidays -13 days per calendar year
Work, Personal and Family Time - up to 40 hours per calendar year
Parental Leave - 480 hours within one year of the birth/adoption/foster care of a child
Bereavement Leave - 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
Caregiver Leave - 80 hours in a 52-week rolling period10 days
Volunteer Leave - 32 hours per calendar year
Military Spouse Time-Off - 80 hours per calendar year
For additional general information on Company benefits, please go to: - https://www.careers.jnj.com/employee-benefits

What Johnson & Johnson employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom