Data Engineer (SMTS/LMTS) - Knowledge Graph & AI
Salesforce1 day ago
Seattle, WA, USA +3 moreSenior / Staff+
H1B Sponsor
Base Salary
$149k - $286k/yr
Responsibilities
- Build and scale Salesforce’s Enterprise Knowledge Graph platform for performance, throughput, reliability, availability, and data integrity.
- Develop graph data models, complex graph queries, enterprise ontologies, taxonomies, semantic layers, entity resolution frameworks, graph APIs, and vector search capabilities.
- Create and productionize Python-based semantic routing frameworks that direct queries to knowledge graph indexes, ontology subgraphs, or vector databases.
- Build scalable pipelines that ingest, transform, map, and orchestrate structured, unstructured, and third-party data into graph platforms.
- Develop, integrate, deploy, and optimize AI-powered developer tools and engineering automation using Claude, Cursor, Windsurf, AI Agents, and MCP frameworks.
- Own platform features from concept through design, coding, testing, and production deployment.
- Participate in code reviews, write automated unit and integration tests, and uphold engineering and operational standards.
- Mentor engineers and provide technical guidance, design direction, and code reviews.
- Collaborate with engineering, product management, data engineering, ontology governance, and principal engineering teams.
- For LMTS, evaluate emerging graph technologies, ontology tools, semantic reasoning frameworks, vector databases, and AI tooling.
Requirements
- SMTS candidates must have 8+ years of hands-on experience in software engineering, data engineering, distributed systems, or enterprise data platforms.
- LMTS candidates must have 10+ years of hands-on experience in software engineering, data engineering, distributed systems, or enterprise data platforms.
- A related technical degree is required.
- Expert backend programming ability and strong fluency in Python plus object-oriented or functional programming languages are required.
- Experience with semantic routers, embeddings, LangChain, RAG architectures, vector search, and AI workflows is required.
- Experience with graph databases, semantic web concepts, ontologies, taxonomies, or enterprise metadata systems is required.
- Experience with Neo4j, RDF/OWL, SPARQL, property graphs, and, for LMTS, tools such as TopQuadrant, Protégé, and SHACL is expected.
- Experience building cloud-native distributed systems using AWS, GCP, or Azure, microservices, REST/gRPC APIs, and event-driven streaming such as Kafka is required.
- Experience integrating AI-assisted engineering tools or automation workflows such as Claude, Cursor, Windsurf, GitHub Copilot, or MCP frameworks is required.
- LMTS candidates must have experience leading feature teams, guiding technical execution, and mentoring mid-to-senior engineers.
- Preferred qualifications include a master’s degree, Salesforce Data Cloud or CRM platform integrations, ontology validation and governance, high-throughput search, graph-powered recommendations, federated knowledge management, and engineering velocity improvements through AI tooling.
Benefits
- Benefits include time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program.
- Certain roles may be eligible for incentive compensation, equity, and additional benefits.
- Salesforce provides reasonable accommodations during the application and recruiting process.
Tech Stack
Categories
BackendData Engineering
About Salesforce
We're the #1 AI CRM—where humans with agents drive customer success together with AI, data, and Customer 360 apps on one platform. Privacy Statement: http://www.salesforce.com/company/privacy/