Data Scientist
Job Description
The Einstein GPT Data Science Team is at the forefront of cutting-edge Generative AI, NLP, and Deep Learning advancements. We develop technologies that power Agentforce, an autonomous AI suite enhancing efficiency across service, sales, marketing, and commerce.
We are seeking a highly skilled Data Scientist to develop and apply advanced retrieval-augmented generation (RAG) technologies, enterprise knowledge graphs, LLM evaluations, and search relevance models. This role will contribute directly to AI-driven automation that scales business operations and improves customer satisfaction.
Key Responsibilities
- Research, develop, and optimize machine learning models for large-scale business applications.
- Work on Natural Language Processing (NLP), deep learning, and generative AI models to improve AI-driven customer experiences.
- Design and fine-tune large language models (LLMs), including prompt engineering and retrieval-based enhancements.
- Implement feedback-based learning systems and guardrails to improve AI accuracy, safety, and efficiency.
- Develop and maintain AI-driven enterprise knowledge graphs to enhance data retrieval and relevance.
- Collaborate with cross-functional teams, including software engineers, AI researchers, and business stakeholders, to integrate models into Salesforce’s AI ecosystem.
- Stay ahead of emerging AI and machine learning trends to drive innovation and best practices in AI-powered CRM solutions.
Skill & Experience
- Ph.D. or a Master’s degree (with 2+ years of experience) in Computer Science, Machine Learning, NLP, AI, or a related field.
- Strong experience in NLP, deep learning, and generative models.
- Expertise in building and applying machine learning models for large-scale applications.
- Proficiency in Python and machine learning frameworks such as TensorFlow or PyTorch.
- Experience deploying AI/ML solutions in production environments at scale.
- Strong problem-solving skills with a track record of delivering AI innovations.
- 2+ years of industry or applied research experience in AI, ML, NLP, or information retrieval.
- Expertise in LLM fine-tuning, prompt engineering, and reinforcement learning techniques.
- Deep understanding of search relevance models, AI safety guardrails, and enterprise AI solutions.
- Experience working with Conversational AI and intelligent virtual assistants.
- Strong knowledge of Bayesian methods, transformer-based architectures, and AI interpretability techniques.
- Publications in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ACL).
- Excellent written and verbal communication skills.
