Job Details
Location: San Francisco, CA 94105
About Salesforce
Salesforce stands as the leading AI powered CRM where agents collaborate to drive customer success. Ambition meets action, and trust accompanies technology. Innovation is a core value lived every day. We are seeking Trailblazers who want to improve business and the world through AI, fueling innovation while upholding Salesforce’s values at the heart of everything we do.
If you are ready to elevate your career at the company transforming the workforce in the agentic era, you’re in the right place. Agentforce represents the future of AI, and you play a part in Salesforce’s future.
The Experience
Salesforce is building a next generation Enterprise Knowledge Graph platform to power AI driven experiences, agentic applications, semantic search, enterprise data discovery, and intelligent decision making across the organization. The platform acts as the foundational knowledge layer that connects enterprise data, business entities, ontologies, and relationships across multiple domains. We are seeking both a Senior Member of Technical Staff (SMTS) and a Lead Member of Technical Staff (LMTS) to join our Enterprise Knowledge Graph and AI Engineering team.
The SMTS will function as a senior engineer and core systems developer, hands-on and focused on developing, optimizing, and scaling core knowledge graph components, semantic pipeline workflows, and AI powered frameworks. You will collaborate with Lead and Principal Engineers to implement technical designs and deliver production-ready, scalable systems that support agentic AI use cases across the enterprise.
The LMTS will act as a hands-on technical lead, systems designer, and ontology engineer, 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 that improve engineering productivity, software quality, and delivery velocity across the organization.
What You'll Actually Be Doing
- Design & Implement: Build and scale Salesforce's Enterprise Knowledge Graph platform components, focusing on performance, data throughput, system 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 & 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 & 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 (MCP) 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 & 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 & Rigor: Participate heavily in code reviews, write comprehensive automated unit/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/Principal Engineers, Product Managers, and Data Engineering teams to deliver robust features aligned with broader enterprise AI priorities. (LMTS: Also partner with PMTS engineers and Ontology governance boards to ensure alignment with AI infrastructure standards.)
- Evaluate & Innovate: 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 required.
- Core Programming: Expert-level coding skills in backend ecosystems, with strong fluency in Python and standard object-oriented/functional programming languages.
- Semantic Routing & AI: Hands-on experience developing and deploying custom semantic routers using Python (leveraging native embeddings, LangChain, or mathematical logic like cosine similarity) alongside RAG architectures, vector search platforms, and AI workflows.
- Graph & Ontology Fundamentals: Solid experience working with graph databases and semantic web concepts (e.g., 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 (e.g., Claude, Cursor, Windsurf, GitHub Copilot, or MCP frameworks).
- Distributed Systems & Cloud: Proven experience building applications on cloud-native systems (AWS, GCP, or Azure) utilizing microservices, REST/gRPC APIs, and event-driven data streaming (e.g., Kafka).
- Delivery: Track record of owning and successfully 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 required.
- Ontology & Graph Expertise: Solid, hands-on experience designing and building Knowledge Graph platforms, formal ontologies, semantic models, taxonomies, or enterprise metadata management systems.
- Tooling & Ecosystems: Strong hands-on experience with graph technologies and ontology engineering tools (e.g., Neo4j, TopQuadrant, Protégé, RDF/OWL, SPARQL, SHACL, property graphs) and semantic reasoning frameworks.
- AI & 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 routing frameworks.
