Salesforce Machine Learning Engineer Agentic Search & Knowledge Graphs 

United States, California, Palo Alto 

369274344

Job Category

Job Details

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In this role, you will drive the development of agentic Search and Knowledge Graph solutions at scale, integrating the latest advancements in machine learning, LLMs, and vector databases. You will be responsible for leading the end-to-end AI lifecycle, from conceptualization through production, focusing on scalable search and retrieval architectures optimized for enterprise use cases. As a motivated leader with a point of view, you will define standard methodologies and collaborate closely with Product Managers, Data Scientists, and Research teams to shape and deliver groundbreaking AI experiences.

• Lead the Architecture of Sophisticated Search & Knowledge Graph Solutions
  Architect and implement end-to-end, large scale search and retrieval solutions that demonstrate Knowledge Graphs and are optimized for high-performance, multi-tenant environments.
• Develop Intelligent Retrieval Pipelines
  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 demand forecasting, configuration, and proactive monitoring, driving real time search optimization.
• Collaborate Across Teams for AI Driven Product Innovation
  Work closely with multi-functional teams, including Product Managers, Knowledge Engineers, and ML Researchers, to assemble requirements and translate them into scalable, innovative 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, valuing continuous innovation.

• 5+ years in Machine Learning & Search Systems
  Extensive experience with large-scale search, Machine Learning, and knowledge driven systems, specifically focused on integrating Knowledge Graphs, search optimization, and sophisticated retrieval techniques.
• 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 applying LLMs to enhance knowledge based search and retrieval in enterprise contexts.
• Sophisticated 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
  Authority understanding of distributed systems, data streaming (e.g., Kafka, Spark), and Machine Learning frameworks (TensorFlow, PyTorch) to support realtime, 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 improve relevance in enterprise-scale systems.
• Graph Embedding & Contextual Retrieval Expertise
  Confirmed 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 confirmed expertise in collaborating across engineering, research, and product teams.

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Posting Statement

does not accept unsolicited headhunter and agency resumes.