Data Engineer (SMTS/LMTS) - Knowledge Graph & AI

About Salesforce

Salesforce leads as the top AI powered CRM, where people and automation collaborate to drive customer success. Our culture blends ambition with action, trust, and practical innovation. We value progress and integrity, and we seek Trailblazers who want to advance business and the world through AI while upholding Salesforce's core principles.

Location: San Francisco, CA 94105

The Experience

Salesforce is building the next generation Enterprise Knowledge Graph platform to power AI driven experiences, agentic applications, semantic search, enterprise data discovery, and intelligent decision making across the company. The platform acts as the foundational knowledge layer linking enterprise data, business entities, ontologies, and relationships across multiple domains.

We are looking for both a Senior Member of Technical Staff (SMTS) and a Lead Member of Technical Staff (LMTS) to join the Enterprise Knowledge Graph and AI Engineering team.

The SMTS will act as a senior engineer and core systems developer, with hands-on responsibilities to develop, optimize, and scale core knowledge graph components, semantic pipelines, and AI powered frameworks. You will collaborate with Lead and Principal Engineers to implement technical designs and build production-ready, scalable systems that support agentic AI use cases enterprise-wide.

The LMTS will serve as a hands-on technical lead, systems designer, and ontology engineer, responsible for designing, building, and scaling core knowledge graph infrastructure, semantic schemas, and AI powered developer frameworks. You will work closely with Principal Engineers, Product Management, Ontology experts, and Data Engineering teams to translate high level engineering visions into production-ready, scalable foundations.

Both roles will actively implement and drive AI powered engineering tools and developer platforms to improve engineering productivity, software quality, and delivery velocity across the organization.

What You'll Actually Be Doing

• Design and implement scalable components of Salesforce's Enterprise Knowledge Graph platform, focusing on performance, data throughput, reliability, high availability, and robust data integrity. (LMTS: Lead hands-on design and implementation of platform subsystems; SMTS: Write high-quality, production-grade code.)
• Graph and Ontology Engineering: develop graph data models, write complex graph queries, and construct scalable data pipelines to ingest and map structured and unstructured data to enterprise ontologies and taxonomies. (LMTS: Also design enterprise ontologies, taxonomies, semantic layers, entity resolution frameworks, graph APIs, and vector search capabilities to support advanced RAG and agentic workflows.)
• Semantic Routing: write and maintain Python based semantic routing frameworks to parse, classify, and dynamically direct incoming queries to the appropriate knowledge graph indexes or vector databases. (LMTS: Design, optimize, and productionize routing frameworks at enterprise scale, steering queries to appropriate knowledge graphs, ontology sub-graphs, or vector databases.)
• AI Tooling and Automation: build, integrate, and leverage AI powered developer tools and engineering automation platforms utilizing ecosystems such as Claude, Cursor, Windsurf, AI Agents, and Model Context Protocol frameworks. (LMTS: Also develop, deploy, and optimize these tools; drive strategy and productionization.)
• Data Integration: build scalable data pipelines and engineering patterns to ingest, transform, and orchestrate structured, unstructured, and third-party data sources into graph-based platforms mapped tightly to enterprise ontologies.
• Feature Ownership and Technical Execution: own the technical execution of specific platform features from concept through design, coding, testing, and production deployment. (LMTS: Also translate high-level technical visions and roadmaps into concrete system blueprints, ontology schemas, and execution plans.)
• Code Quality and Rigor: participate in code reviews, write comprehensive automated unit and integration tests, and ensure adherence to engineering standards and operational best practices.
• Technical Mentorship: provide technical guidance and mentorship to engineers on the team. (SMTS: mentor MTS and Associate engineers. LMTS: provide day-to-day guidance, code reviews, and design direction to SMTS, MTS, and associate engineers, fostering a culture of technical rigor and operational maturity.)
• Cross-Functional Collaboration: work closely with Lead and Principal Engineers, Product Managers, and Data Engineering teams to deliver robust features aligned with enterprise AI priorities. (LMTS: also partner with PMTS engineers and Ontology governance boards to ensure alignment with AI infrastructure standards.)
• Evaluate and Innovate (LMTS): conduct deep-dive evaluations of emerging graph technologies, ontology modeling tools, semantic reasoning frameworks, vector databases, and AI tooling to continuously modernize the platform.

You're Our Person If

SMTS

• Experience: 8+ years of hands-on software engineering experience in development, data engineering, distributed systems, or enterprise data platforms.
• Education: A related technical degree is required.
• Core Programming: Expert-level backend coding skills with fluency in Python and standard object-oriented or functional programming languages.
• Semantic Routing and AI: Hands-on experience building and deploying custom semantic routers using Python, leveraging native embeddings, LangChain, or cosine similarity, alongside RAG architectures, vector search platforms, and AI workflows.
• Graph and Ontology Fundamentals: Solid experience with graph databases and semantic web concepts (Neo4j, RDF/OWL, SPARQL, property graphs) and mapping data to structured taxonomies.
• Developer Tooling: Practical experience configuring, testing, or integrating AI assisted engineering tools or automation workflows (Claude, Cursor, Windsurf, GitHub Copilot, or MCP frameworks).
• Distributed Systems and Cloud: Proven experience building cloud-native applications (AWS, GCP, or Azure) using microservices, REST/gRPC APIs, and event-driven data streaming (Kafka).
• Delivery: Track record of owning and delivering complex features in an agile, production-scale environment.

LMTS

• Experience: 10+ years of hands-on experience in software engineering, data engineering, distributed systems, or enterprise data platforms.
• Education: A related technical degree is required.
• Ontology and Graph Expertise: Substantial hands-on experience designing and building Knowledge Graph platforms, formal ontologies, semantic models, taxonomies, or enterprise metadata management systems.
• Tooling and Ecosystems: Strong hands-on experience with graph technologies and ontology engineering tools (Neo4j, TopQuadrant, Protégé, RDF/OWL, SPARQL, SHACL, property graphs) and semantic reasoning frameworks.
• AI and Retrieval: Proven experience implementing graph-powered AI solutions, vector search platforms, Retrieval-Augmented Generation (RAG) architectures, and orchestrating agentic workflows.
• Semantic Routing Mastery: Demonstrated hands-on experience designing, optimizing, and productionizing custom semantic r