Machine Learning Architect - RAG Foundations

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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.

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.

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.

• 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.

• 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, retriever-reranker architectures, and integration with LLMs for scalable, accurate information retrieval.

• Expertise in Semantic and Vector-Based Search
  Deep knowledge of vector databases (e.g., FAISS, Pinecone, Milvus), approximate nearest neighbor (ANN) search algorithms, and embedding techniques to power high-relevance search systems.
• Strong Background in NLP & LLMs
  Experience with natural language processing (NLP), prompt engineering, and utilizing LLMs to enhance knowledge-based search and retrieval in enterprise contexts.
• Advanced Knowledge Graph Skills
  Proficiency in graph databases (e.g., Neo4j, Amazon Neptune), graph embedding, and linking techniques to enable rich contextual search and high-dimensional graph-based retrieval.
• Proficiency in Distributed Systems & ML Frameworks
  Advanced understanding of distributed systems, data streaming (e.g., Kafka, Spark), and ML frameworks (TensorFlow, PyTorch) to support real-time, resilient AI applications.
• Programming Mastery in Python & Graph-Based Frameworks
  Strong programming skills in Python, with expertise in machine learning and graph-based frameworks to facilitate scalable, high-performance AI solutions.

• Experience with Multi-Stage Retrieval Pipelines
  Hands-on experience in designing and optimizing multi-stage retrieval workflows that balance precision, recall, and relevance at scale.
• In-Depth Knowledge of Re-Ranking & Retrieval Optimization
  Expertise in retrieval-specific optimizations, including re-ranking, hybrid search, and knowledge-augmented retrieval, to maximize relevance in enterprise-scale systems.
• Graph Embedding & Contextual Retrieval Expertise
  Proven skills in graph-based search, context expansion techniques, and Knowledge Graph integration to enhance retrieval depth and accuracy.
• Knowledge Graph Curation & Ontology Management
  Experience in Knowledge Graph curation, schema design, and ontology management, ensuring efficient and adaptable knowledge-driven search solutions.
• Familiarity with Feedback Loops and Fine-Tuning
  Knowledge of incorporating user feedback and relevance signals to fine-tune contextual embeddings and improve Search and Knowledge Graph system performance.

• Broad ML Experience with Diverse Approaches
  Strong foundation in diverse ML techniques, from neural networks to probabilistic models, adaptable for Search and Knowledge Graph-centric AI use cases.
• Exceptional Communication and Collaboration Skills
  Outstanding written and verbal communication abilities, with demonstrated expertise in collaborating across engineering, research, and product teams.

If you're an industry leader passionate about RAG, Search, Knowledge Graphs, and cutting-edge AI, and eager to make an impact at the world's #1 CRM company, we'd love to meet you!

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