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!

#J-18808-Ljbffr

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.
Job Category:  Software Engineering
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.
We're seeking exceptional Machine Learning Engineers to advance Agentforce's agent capabilities. In this role, you'll develop fully-autonomous agents with planning, reasoning, and verifiable action execution, while enabling tool use, computer use, and code execution at scale, developing memory architecture, and multi-agent interactions. You'll drive innovation at the intersection of research, incubation and product. By creating state-of-the-art solutions, you'll transform how organizations leverage AI in their workflows, applying your expertise to solve real enterprise challenges and shape the future of AI technology.
Responsibilities  Agentic AI System Backend/Frontend Development:   Develop agentic system infrastructure and components, including planning and reasoning engines, tool use, computer use and code execution environments, memory architecture, multi-agent orchestration layer, human-agent interaction, etc.
Design, implement, and tune robust APIs and API framework-related features.
Create visualization interfaces for system demonstration and rapid development iteration.
Prompt Engineering and Optimization:  Design and experiment with creative prompts to optimize the performance of LLMs for specific use cases, ensuring accuracy, relevance, and efficiency. Implement techniques like few-shot learning, chain-of-thought prompting, and context tuning to enhance LLM outcomes for AI agents.
AI Model Development, Evaluation and Deployment:   Finetune AI model for agentic use case.
Develop metrics and tools to evaluate model performance, reliability, and trust.
Deploy LLM models into production systems, ensuring scalability, security, and efficiency.
Collaboration and Research:  As a machine learning engineer from the AI research team, you collaborate with researchers, product managers, engineers, and designers to identify and implement AI-driven solutions for internal incubators. Stay at the forefront of LLM research and emerging technologies to recommend and integrate advancements into Salesforce’s AI strategy.
Educational Background:  Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Machine Learning, or a related field.
Technical Skills:   Strong expertise in LLMs/MLMs (e.g., GPT, Gemini, Claude, Llama, Mistral, Qwen, Deepseek, etc.) and prompt engineering techniques.
High proficiency in Python programming language. Experience in Java is a plus.
Preferred Qualifications:   Experience in developing complex agentic systems using LLMs.
Experience in REST-based API development, API lifecycle management and/or client SDKs development.
Experience in frontend development for system demonstration, such as using tools like Streamlit, Chainlit, etc.
Experience with large-scale reinforcement learning techniques.
Proficiency in popular Deep Learning frameworks (e.g., PyTorch, TensorFlow, Hugging Face Transformers).
Prior experience in building AI solutions for enterprise software or SaaS platforms.
If you require assistance due to a disability applying for open positions please submit a request via this  Accommodations Request Form .
At Salesforce we believe that the business of business is to improve the state of our world. Each of us has a responsibility to drive Equality in our communities and workplaces. We are committed to creating a workforce that reflects society through inclusive programs and initiatives such as equal pay, employee resource groups, inclusive benefits, and more. Learn more about Equality at  www.equality.com  and explore our company benefits at  www.salesforcebenefits.com .
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 California-based roles, the base salary hiring range for this position is $172,000 to $236,500. 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:  https://www.salesforcebenefits.com .

#J-18808-Ljbffr

We're seeking exceptional Machine Learning Engineers to advance Agentforce's agent capabilities. In this role, you'll develop fully-autonomous agents with planning, reasoning, and verifiable action execution, while enabling tool use, computer use, and code execution at scale, developing memory architecture, and multi-agent interactions. You'll drive innovation at the intersection of research, incubation and product. By creating state-of-the-art solutions, you'll transform how organizations leverage AI in their workflows, applying your expertise to solve real enterprise challenges and shape the future of AI technology.
Responsibilities    Agentic AI System Backend/Frontend Development:      Develop agentic system infrastructure and components, including planning and reasoning engines, tool use, computer use and code execution environments, memory architecture, multi-agent orchestration layer, human-agent interaction, etc.

Design, implement, and tune robust APIs and API framework-related features.

Create visualization interfaces for system demonstration and rapid development iteration.

Prompt Engineering and Optimization:    Design and experiment with creative prompts to optimize the performance of LLMs for specific use cases, ensuring accuracy, relevance, and efficiency. Implement techniques like few-shot learning, chain-of-thought prompting, and context tuning to enhance LLM outcomes for AI agents.
AI Model Development, Evaluation and Deployment:      Finetune AI model for agentic use case.

Develop metrics and tools to evaluate model performance, reliability, and trust.

Deploy LLM models into production systems, ensuring scalability, security, and efficiency.

Collaboration and Research:    As a machine learning engineer from the AI research team, you collaborate with researchers, product managers, engineers, and designers to identify and implement AI-driven solutions for internal incubators. Stay at the forefront of LLM research and emerging technologies to recommend and integrate advancements into Salesforce’s AI strategy.
Educational Background:    Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Machine Learning, or a related field.
Technical Skills:      Strong expertise in LLMs/MLMs (e.g., GPT, Gemini, Claude, Llama, Mistral, Qwen, Deepseek, etc.) and prompt engineering techniques.

High proficiency in Python programming language. Experience in Java is a plus.

Preferred Qualifications      Experience in developing complex agentic systems using LLMs.

Experience in REST-based API development, API lifecycle management and/or client SDKs development.

Experience in frontend development for system demonstration, such as using tools like Streamlit, Chainlit, etc.

Experience with large-scale reinforcement learning techniques.

Proficiency in popular Deep Learning frameworks (e.g., PyTorch, TensorFlow, Hugging Face Transformers).

Prior experience in building AI solutions for enterprise software or SaaS platforms.

#J-18808-Ljbffr

Machine Learning Engineer Salesforce Jobs

Search for Job title, position, company and/or location…

Machine Learning Engineer Agentic Search Knowledge Graphs
April 3, 2025

Senior Machine Learning Engineer Ai Research
March 30, 2025

Senior Machine Learning Engineer Ai Research
March 22, 2025

Quick filters

Top companies