Graph Database Engineer Job at Leading Path Consulting (Chantilly)

Learning Process Engineer

This is a career-defining opportunity to play a crucial role in a hyper-scale AI...

Location

United States , Salt Lake City

Salary:

Not provided

PassiveLogic

Expiration Date

Until further notice

Requirements

Technical background in computer science, AI/ML, data engineering, or knowledge systems
Experienced with graph databases (Neo4j, TigerGraph, Weaviate, Neptune), Python/C++, graph query languages (Cypher, Gremlin, GraphQL, SPARQL), graph ML/embeddings, and building ETL pipelines, event-driven systems, and real-time feedback loops
Understanding of feedback-driven model improvement, reinforcement learning, or adaptive systems
Experience working cross-functionally with engineers, designers, and product managers
Analytical mindset: ability to define success metrics, run experiments, and interpret results
Excellent communication skills and a collaborative, problem-solving approach
Background in process engineering, systems design, product operations, or applied AI/ML
Strong systems thinking: ability to model complex workflows and simplify them into actionable processes
Familiarity with human-in-the-loop learning, adaptive systems, or feedback-driven workflows
Proven experience: 5+ years in developing software with an ecosystem nature

Job Responsibility

Architect feedback pipelines: Build and maintain data ingestion and labeling processes that transform user interactions into structured learning signals
Design graph-based knowledge structures: Model, update, and optimize workflows in a graph database (e.g., Neo4j, ArangoDB, Weaviate, or similar)
Implement adaptive logic: Use graph queries and embeddings to inform recommendations, predictions, and workflow adaptation
Integrate human-in-the-loop learning: Deploy mechanisms that incorporate user corrections and contextual feedback into graph representations and model updates
Collaborate with ML and software engineers: Define retraining strategies, model evaluation criteria, and experiment frameworks that leverage graph-based data
Automate performance monitoring: Develop dashboards and metrics for tracking how graph-driven learning impacts system accuracy, adoption, and efficiency

What we offer

Competitive compensation
Generous equity share package
Medical, dental and vision coverage
Disability and life Insurance options
Flex PTO
Team-building events
Free catered lunch in the office Monday — Friday
Free ski pass (We are at the base of Big Cottonwood Canyon)
Free National Park pass
Onsite Gym

Fulltime

Learning Process Engineer

We’re building the next generation of AI-powered productivity tools for autonomo...

Location

Netherlands , Amsterdam

Salary:

Not provided

PassiveLogic

Expiration Date

Until further notice

Requirements

Technical background in computer science, AI/ML, data engineering, or knowledge systems
Experienced with graph databases (Neo4j, TigerGraph, Weaviate, Neptune), Python/C++, graph query languages (Cypher, Gremlin, GraphQL, SPARQL), graph ML/embeddings, and building ETL pipelines, event-driven systems, and real-time feedback loops
Understanding of feedback-driven model improvement, reinforcement learning, or adaptive systems
Experience working cross-functionally with engineers, designers, and product managers
Analytical mindset: ability to define success metrics, run experiments, and interpret results
Excellent communication skills and a collaborative, problem-solving approach
Background in process engineering, systems design, product operations, or applied AI/ML
Strong systems thinking: ability to model complex workflows and simplify them into actionable processes
Familiarity with human-in-the-loop learning, adaptive systems, or feedback-driven workflows
Proven experience: 5+ years in developing software with an ecosystem nature

Job Responsibility

Architect feedback pipelines: Build and maintain data ingestion and labeling processes that transform user interactions into structured learning signals
Design graph-based knowledge structures: Model, update, and optimize workflows in a graph database (e.g., Neo4j, ArangoDB, Weaviate, or similar)
Implement adaptive logic: Use graph queries and embeddings to inform recommendations, predictions, and workflow adaptation
Integrate human-in-the-loop learning: Deploy mechanisms that incorporate user corrections and contextual feedback into graph representations and model updates
Collaborate with ML and software engineers: Define retraining strategies, model evaluation criteria, and experiment frameworks that leverage graph-based data
Automate performance monitoring: Develop dashboards and metrics for tracking how graph-driven learning impacts system accuracy, adoption, and efficiency

What we offer

Competitive compensation
Generous equity share package
Pension plan
Paid time off
Commute Coverage (NS Business Card or Car allowance)
In Office Lunch
Fun office-wide activities quarterly
Worldwide ski/snowboard pass

Staff Software Engineer, Social Graph

We are seeking a Staff Software Engineer with deep expertise in graph theory, gr...

Location

United States , San Francisco

Salary:

181000.00 - 271000.00 USD / Year

GoFundMe

Expiration Date

Until further notice

Requirements

8+ years of industry experience, including significant experience at senior / staff / principal levels
Demonstrated expertise launching and scaling graph-based applications in production
Deep understanding of graph theory, graph algorithms (e.g., traversal, clustering, centrality), and modern graph data structures
Expert-level experience with graph databases (Neo4j, TigerGraph, JanusGraph, DGL-backed systems, etc.) and efficient graph querying
Proven ability to design high-scale pipelines for ingesting and transforming social or behavioral data
Experience with distributed streaming frameworks (Kafka, Flink, Spark Streaming)
Hands-on experience incorporating graph-derived features into recommendation, ranking, trust, or safety models
Familiarity with Graph Neural Networks (GNNs), graph embeddings, or graph-based ranking systems
Strong product intuition and ability to articulate how graph systems drive business outcomes
Ability to influence architectural direction and mentor teams

Job Responsibility

Serve as the technical lead for initiatives related to social graph modeling, storage, retrieval, and computation
Architect and scale graph databases and graph query systems capable of supporting billions of nodes and edges with low-latency performance
Design and ship pipelines for ingesting, cleaning, and transforming social and behavioral data into graph structures
Partner with ML teams to productionize graph-based features, including embeddings, similarity signals, trust metrics, and GNN-powered ranking features
Lead the development of graph-informed recommendation, trust, and safety systems, ensuring models reflect real-world connectivity patterns
Define and implement feature engineering strategies leveraging graph topology (e.g., mutual connections, influence scoring, community structure)
Contribute to architecture decisions related to streaming systems (Kafka, Flink, Spark Streaming) and real-time graph updates
Mentor engineers and guide best practices on graph design, distributed systems, feature computation, and ML integration
Collaborate with Product to translate graph capabilities into business-impacting features that drive trust, engagement, and discovery
Ensure reliability, scalability, observability, and data quality in all graph-related systems

What we offer

Competitive pay and comprehensive healthcare benefits
Financial assistance for things like hybrid work, family planning
Generous parental leave
Flexible time-off policies
Mental health and wellness resources
Learning, development, and recognition programs
Volunteering program
Equity

Fulltime

Senior Data Engineer (Graph)

As a Senior Data Engineer, you will play a pivotal role in transforming data int...

Location

United States , San Francisco

Salary:

90.00 - 93.00 USD / Hour

Software Resources

Expiration Date

Until further notice

Requirements

5+ years of data engineering experience developing data pipelines
Understanding core concepts of graph databases and its advantages over a traditional RDBMS for modeling data. Knowing use cases.
Proficiency in at least one major programming language (e.g., Python)
ETL development for graph databases (extracting or loading into a graph databases)
Hands-on production experience with data pipeline orchestration systems such as Airflow for creating and maintaining data pipelines
Experience Neo4j with Snowflake
Strong algorithmic problem-solving expertise
Comfortable working in a fast-paced and highly collaborative environment.
Excellent written and verbal communication
Willingness and ability to learn and pick up new skill sets

Job Responsibility

Create and maintain Data Platform pipelines
supporting structured, graph, and unstructured datasets
Architect and implement graph database models, schema design, and build robust, scalable solutions.
Fluency with data engineering concepts and platforms (AWS: S3, Lambda, SNS, SQS…
Iceberg), data platforms (Snowflake), configuration (data contracts), transformation, orchestration (dbt, Airflow), data quality (Great Expectations, Anomalo, Soda, Collibra).
Be an active participant and advocate of agile/scrum ceremonies to collaborate and improve processes for our team
Collaborate with product managers, architects, and other engineers to drive the success of the Core Data Platform
Document standards and best practices for pipeline configurations, naming conventions, etc.
Ensure high operational efficiency and quality of the Core Data Platform datasets to ensure our solutions meet SLAs and project reliability and accuracy to all our stakeholders (Engineering, Data Science, Operations, and Analytics teams)
Engage with and understand our customers, forming relationships that allow us to understand and prioritize both innovative new offerings and incremental technology improvements

What we offer

medical, dental, and vision coverage
a 401(k) with company match
short-term disability
life insurance with AD&D

Fulltime

Staff Software Engineer

GEICO is seeking an experienced Staff Software Engineer to join our Knowledge Gr...

Location

United States , Chevy Chase; Seattle

Salary:

105000.00 - 230000.00 USD / Year

Geico

Expiration Date

Until further notice

Requirements

Proven experience designing and implementing knowledge management platforms, semantic data systems, content generation tools, or AI-driven developer platforms
Full-stack developer with extensive experience in modern front-end frameworks (React, TypeScript), web technologies (JavaScript, HTML, CSS/SASS), backend languages (Node.js, Python, Java), and cloud platforms (Azure, AWS, GCP)
Strong ability to architect distributed semantic systems and graph-based microservice architectures that handle complex data relationships and scale reliably
Experience with knowledge graphs, semantic technologies, and AI/ML platforms such as Neo4j, Apache Jena, TigerGraph, or similar graph databases, along with NLP frameworks and content generation models
Familiarity with semantic web standards (RDF, OWL, SPARQL), ontology design, knowledge representation, and automated reasoning systems
Deep understanding of content management ecosystems, headless CMS architectures, API-driven publishing workflows, and content delivery optimization
Experience with AI/ML frameworks for natural language processing, content generation (GPT, BERT, T5), recommendation systems, and knowledge extraction from unstructured data
Product mindset and passion for building intelligent tools that solve complex content challenges and enhance user experiences through semantic understanding
Excellent collaboration and communication skills with ability to explain complex semantic concepts to technical and non-technical stakeholders
In-depth knowledge of CS data structures, algorithms, particularly graph algorithms, semantic matching, and distributed system design patterns

Job Responsibility

Architect and design enterprise-scale knowledge graph platforms that capture and model GEICO's comprehensive insurance domain expertise, customer insights, product relationships, and market intelligence
Build automated semantic content generation systems that leverage knowledge graphs to create personalized insurance content, product descriptions, educational materials, and customer communications at scale
Develop intelligent content workflows and APIs that use graph traversal algorithms, natural language processing, and machine learning to automate content production, template generation, and multi-channel publishing
Design real-time content personalization engines that query knowledge graphs to deliver contextually relevant messaging based on customer profiles, policy information, and behavioral patterns
Create sophisticated data ingestion and enrichment pipelines that continuously build and maintain knowledge graphs from structured and unstructured data sources across the enterprise
Implement semantic search and content discovery platforms that understand customer intent and context through graph-based query processing and recommendation algorithms
Build internal dashboards and tooling for content performance monitoring, knowledge graph visualization, semantic relationship analysis, and content optimization insights
Lead cross-functional collaboration with product managers, data scientists, and content strategists to translate business objectives into scalable knowledge-driven technical solutions
Champion engineering excellence in semantic modeling, ontology design, graph database optimization, and AI/ML integration best practices
Mentor engineering teams on knowledge graph technologies, content automation frameworks, and distributed system design patterns for semantic platforms

What we offer

Comprehensive Total Rewards program that offers personalized coverage tailor-made for you and your family’s overall well-being
Financial benefits including market-competitive compensation
a 401K savings plan vested from day one that offers a 6% match
performance and recognition-based incentives
and tuition assistance
Access to additional benefits like mental healthcare as well as fertility and adoption assistance
Supports flexibility- We provide workplace flexibility as well as our GEICO Flex program, which offers the ability to work from anywhere in the US for up to four weeks per year

Fulltime

Staff AI Context Engineer

MagicSchool is seeking a Staff AI Context Engineer to architect and enhance the ...

Location

United States

Salary:

205000.00 - 240000.00 USD / Year

EdTech Jobs

Expiration Date

Until further notice

Requirements

Deep Knowledge Systems Experience: 5+ years building large-scale information systems with at least 2+ years in staff/senior roles. Extensive hands-on experience with RAG systems, knowledge graphs, or semantic search platforms in production environments.
Graph Database Expertise: Deep experience with graph databases (Neo4j, Neptune, or similar), including schema design, query optimization (Cypher, Gremlin), and building graph-based applications.
RAG & Retrieval Mastery: Demonstrated expertise building production RAG systems including embedding selection, chunking strategies, hybrid search, reranking, and retrieval evaluation. Familiarity with vector databases (pgvector, Pinecone, Weaviate, Qdrant).
Embedding & NLP Background: Strong understanding of embedding models (sentence transformers, domain-specific embeddings), fine-tuning approaches, and semantic similarity. Experience with document processing, entity extraction, and text chunking for optimal retrieval.
Technical Stack: Strong coding skills in Python and/or TypeScript/Node.js. Experience with our stack (TypeScript, Node.js, PostgreSQL, NextJS, Supabase) plus graph databases and vector stores. Familiarity with LLM APIs and context management patterns.
Information Architecture: Deep understanding of information retrieval theory, semantic search, knowledge representation, and strategies for organizing complex domain knowledge for both human and AI consumption.
Leadership & Impact: Track record of architecting complex knowledge systems, making high-leverage technical decisions about information architecture, and mentoring engineers on sophisticated retrieval and graph concepts.

Job Responsibility

Knowledge Graph & Semantic Architecture: Architect and implement graph-based knowledge systems (Neo4j, Neptune, etc) that represent educational content relationships, standards alignments, prerequisite chains, curriculum coherence, learning progressions, and pedagogical connections.
Graph Schema & Ontology Development: Design and evolve ontologies and schemas for educational content, defining entity types (standards, concepts, skills, assessments), relationship semantics, and property models.
GraphRAG Implementation: Build GraphRAG systems that combine knowledge graph traversal with vector similarity, enabling agents to retrieve contextually connected educational materials.
Retrieval Pipeline Architecture: Architect and implement sophisticated retrieval-augmented generation pipelines including hybrid search (dense + sparse), multi-stage retrieval, reranking strategies, and query understanding.
Embedding & Vectorization Strategy: Design and operationalize embedding pipelines for educational content, selecting and fine-tuning embedding models, implementing chunking strategies, and managing vector stores at scale.
Retrieval Evaluation & Optimization: Design evaluation pipelines that measure retrieval precision, recall, MRR, and NDCG across educational content types. Continuously optimize retrieval quality.
Document Ingestion & Processing: Build robust ingestion systems that process structured and unstructured educational content, extracting entities, relationships, and metadata for knowledge base population.
Semantic Parsing & Extraction: Implement NLP pipelines for educational content that extract key concepts, prerequisite relationships, learning objectives, and pedagogical metadata.
Memory & Context Management: Invent and operationalize memory compaction mechanisms, session state management, and cross-conversation memory patterns that allow agents to maintain coherence across extended teaching workflows.
Context Evaluation & Monitoring: Design evaluation frameworks that measure retrieval precision, token relevance, attention allocation, and reasoning coherence as context evolves across sessions.

What we offer

Flexibility of working from home.
Unlimited time off.
Choice of employer-paid health insurance plans. Dental and vision are also offered at very low premiums.
Generous stock options, vested over 4 years.
401k match.
Monthly wellness stipend.

Fulltime

Senior Security Graph Engineer

The Defender Experts (DEX) Research team is at the forefront of Microsoft’s thre...

Location

India , Hyderabad

Salary:

Not provided

Microsoft Corporation

Expiration Date

Until further notice

Requirements

4+ years of experience in security research, detection engineering, threat lifecycle, cloud security in large-scale in complex cloud environments
Strong understanding of graph theory, graph databases (e.g., Neo4j, TigerGraph), and graph analytics with proficiency in Python or similar languages for data analysis and prototyping
Experience working with large-scale datasets, distributed systems and graph analytics projects
Ability to translate complex threat data into graphs and actionable insights
Experience with machine learning or statistical modelling applied to graph data
Proven ability to lead and execute advanced research on emerging cloud-based threats affecting both Microsoft and third-party security products across heterogeneous cloud environments
Knowledge of adversary infrastructure tracking, malware analysis, or campaign clustering
Extensive hands-on experience with cloud platforms—including, but not limited to, Azure—as well as a deep understanding of multi-cloud security challenges and solutions
B. Tech or Equivalent

Job Responsibility

Design and maintain scalable threat graphs that model entities such as devices, identity, threat actors, TTPs, infrastructure, and campaigns
Lead and execute advanced research to develop algorithms and heuristics to detect malicious patterns and relationships within graph data on emerging cloud-based threats impacting Microsoft and third-party security products across heterogeneous cloud environments
Collaborate with threat protection researchers, data scientists, and detection engineers to enrich graph models with contextual insights and refine detection and response strategies, to provide comprehensive threat coverage and response capabilities
Research and prototype novel graph-based techniques for threat detection, attribution, and prioritization in collaboration with internal and external security teams
Translate complex raw security data into actionable graph intelligence that enhances the effectiveness of security operations for a global customer base
Mentor, guide, and drive best practices among researchers and detection engineers on advanced graph-based threat hunting and incident response across diverse ecosystems
Contribute to industry knowledge and Microsoft’s security posture by publishing research, developing threat graph models, and proactively identifying threats and attack trends in the cloud

Fulltime

Data Scientist : Graph Database & Ontology Specialist

We are seeking a Data Scientist with deep expertise in Knowledge Graphs and Onto...

Location

United Kingdom , Bristol

Salary:

Not provided

Tekever

Expiration Date

Until further notice

Requirements

Graph Databases: Advanced Neo4j expertise, including architecture, drivers, administration, and Cypher
Ontology & Semantics: Strong experience with data modeling, ontologies, and semantic technologies (RDF, OWL, SPARQL)
Programming: High proficiency in Python (pandas, networkx, py2neo, neo4j-driver)
Graph ML: Experience with Neo4j GDS or frameworks such as PyTorch Geometric or DGL
Production Engineering: Hands-on experience with Docker, REST APIs (FastAPI/Flask), and CI/CD pipelines
Core Data Science Profile: 3+ years of experience in Data Science or Data Engineering
Experience with NLP for entity and relationship extraction is a plus
Strongly skilled in standard ML workflows (Scikit-Learn, XGBoost)
Experience with geospatial data (GIS, GeoPandas) is valued
Education: MSc in Computer Science, Data Science, or a related engineering field (PhD welcome, but practical delivery is prioritized)

Job Responsibility

Ontology Design & Management: Design and maintain scalable ontologies to unify mission data, sensor outputs, flight logs, and operational parameters
Graph Engineering (Neo4j): Implement, optimize, and operate Neo4j schemas
write high-performance Cypher queries and ensure production scalability
Graph Data Science: Apply graph algorithms (e.g., centrality, pathfinding, community detection) and graph ML to derive actionable insights
Production Deployment: Move solutions from research to production (TRL > 6)
integrate graph models into APIs and pipelines with reliability and latency constraints
Data Integration: Build ingestion pipelines for structured and unstructured data into the Knowledge Graph
Cross-Functional Collaboration: Translate operational and domain requirements into robust data and graph models

What we offer

An excellent work environment and an opportunity to make a difference
Salary Compatible with the level of proven experience

Fulltime

Graph Database Engineer

Leading Path Consulting

Location:
United States , Chantilly

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Job Description:

Job Responsibility:

Requirements:

Nice to have:

Additional Information:

Job Posted:
January 22, 2026

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