Machine Learning Engineer (Agentic Search & Knowledge Graphs)

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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.

Machine Learning Engineer - AgenticSearch & Knowledge Graphs

Salesforce is seeking a visionary Machine Learning Engineer to lead advancements in agentic Search and Knowledge Graph solutions within our Einstein Foundation team. Do you want to build next-gen Generative AI platforms for Empowering Enterprise Wide Knowledge Discovery, Accelerating AI with Knowledge Driven Context, Human Like Understanding of Relationships and Context using Knowledge graphs ?

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 redefining how we enable innovative, knowledge driven experiences across Salesforce's next-gen AI products. As an authority in 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 values innovation, multi-functional collaboration, and a commitment to scaling AI driven customer success solutions.

The Role

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.

What You'll Do:

• 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.

Required Skills:

• 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.

Preferred Search & Knowledge Graph-Specific Skills:

• 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.

Additional Preferred Skills:

• 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.

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

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Posting Statement

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Salesforce is an Equal Employment Opportunity and Affirmative Action Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, or disability status. Salesforce does not accept unsolicited headhunter and agency resumes. Salesforce will not pay any third-party agency or company that does not have a signed agreement with Salesforce.

Salesforce welcomes all.

Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.

For Washington-based roles, the base salary hiring range for this position is $184,000 to $351,800.

For California-based roles, the base salary hiring range for this position is $200,800 to $384,100.

Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, benefits. More details about our company benefits can be found at the following link: