Machine Learning Architect - RAG Foundations

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Job Category

Software Engineering

Job Details

About Salesforce

We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you’ve come to the right place.

Salesforce is seeking a visionary Machine Learning Architect to lead advancements in RAG solutions within our Einstein Foundation team. Do you want to build next-gen Agentforce AI Agents for Empowering Enterprise-Wide Knowledge Discovery, Accelerating AI with Knowledge-Driven Context, Human-Like Understanding of Relationships and Context using Knowledge graphs using Advanced RAG techniques ?

Salesforce, the world’s #1 AI CRM, has recently unveiled Agentforce, a groundbreaking suite of autonomous AI agents that augment employees and handle tasks in service, sales, marketing, and commerce, driving unprecedented efficiency and customer satisfaction.

This role is pivotal in transforming how we enable cutting-edge, knowledge-driven experiences across Salesforce’s next-gen AI products. As an expert in RAG, Search, Knowledge Graphs, and Large Language Models (LLMs), you will drive the evolution of Salesforce's AI systems with innovative retrieval, representation, and context expansion technologies that serve millions of users globally.
 

The Team

Our Einstein Foundation team is an interdisciplinary mix of machine learning engineers, data scientists, and software engineers working collaboratively to build adaptive, context-aware systems that elevate customer interactions and insights. Our team culture values innovation, cross-functional collaboration, and a commitment to scaling AI-driven customer success solutions.
 

The Role

In this role, you will architect and drive the development of RAG and Search solutions at scale, integrating the latest advancements in machine learning, LLMs, and vector databases. You’ll be responsible for leading the end-to-end AI lifecycle, from ideation through production, focusing on scalable search and retrieval architectures optimized for enterprise use cases. As a thought leader, you will define best practices and collaborate closely with Product Managers, Data Scientists, and Research teams to shape and deliver groundbreaking AI experiences.
 

What You’ll Do:

• Lead the Architecture of Advanced Search & Knowledge Graph Solutions
  Architect and implement end-to-end, large-scale search and retrieval solutions that leverage Knowledge Graphs and are optimized for high-performance, multi-tenant environments.
• Imagine and develop next-gen RAG platform features
  Innovate hybrid retrieval pipelines combining semantic, vector, and symbolic search to improve contextual relevance, speed, and accuracy in knowledge-driven AI applications.
• Optimize and Automate Search Systems
  Enhance system efficiency through automation in capacity planning, configuration, and proactive monitoring, driving real-time search optimization.
• Collaborate Across Teams for AI-Driven Product Innovation
  Work closely with cross-functional teams, including Product Managers, Knowledge Engineers, and ML Researchers, to capture requirements and translate them into scalable, cutting-edge search and retrieval solutions.
• Pioneer Search and Knowledge Graph Innovations
  Guide discussions on emerging technologies and advancements in vector search, graph embeddings, and knowledge-augmented retrieval, fostering a culture of continuous innovation.

Required Skills:

• 15+ years in Machine Learning & Search Systems
  Extensive experience with large-scale search, ML, and knowledge-driven systems, specifically focused on integrating Knowledge Graphs, search optimization, and advanced retrieval techniques.
• Extensive background in RAG platform

Deep expertise in Retrieval-Augmented Generation (RAG) platforms, including vector databases, re