Top Natural Language Processing (NLP) Jobs in San Francisco Bay Area, CA

Lead development and implementation of conversational and agentic AI solutions for contact centers. Manage cross-functional teams, translate client requirements into technical specs, design responsive UIs using AngularJS/React/Vue, run usability testing, apply human-centered design, and mentor junior staff while building client relationships to ensure successful delivery of AI-driven products.

Lead design and implementation of conversational and agentic AI solutions for clients, building voice and natural language interfaces, prototypes, and responsive web front-ends. Conduct user research and usability testing, collaborate with cross-functional teams to integrate AI platforms (Dialogflow, Amazon Lex, Azure OpenAI, AWS Bedrock), and apply human-centered design to optimize contact center workflows and customer experiences.

Lead development and implementation of AI-powered contact center and conversational solutions. Guide cross-functional teams to design natural language interfaces using Dialogflow and Amazon Lex, prototype UIs with Figma, conduct usability testing and user research, validate data integrity and security, build stakeholder relationships, and coach teams to deliver high-quality, client-aligned AI products.

Lead research on small language models, develop training methodologies, optimize architectures, ensure AI safety and mentor teams.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

The AI Conversation Designer will create and maintain AI customer support experiences, focusing on chatbot dialogue, QA processes, and integrations, using analytics and best practices.

As an Applied Research Engineer, you will enhance NLP systems and build LLM-enabled products, running experiments and integrating advanced techniques into software systems.

Own and evolve revenue calibration models tying CX interactions to revenue, design causal inference and experimentation frameworks, build LLM-powered classification/NLP pipelines, productionize models with Analytics Engineers, define segmentation and behavioral signals, and ensure statistical rigor for executive reporting and regulatory defensibility.

Build and ship production LLM workflows for contract review, redlining, extraction, and legal Q&A. Design agent orchestration and multi-step reasoning pipelines, diagnose and fix model failures, work with customers to turn legal problems into reliable AI systems, and own features end-to-end from design to production.

Design, build, and productionize LLM-based conversational and AI systems for enterprise coaching. Integrate structured and unstructured data, prototype end-to-end services, improve model behavior and observability, and collaborate with product and engineering to deploy scalable, reliable AI-driven coaching workflows.

The Senior AI Engineer will design and develop voice agents and agentic workflows for healthcare operations, ensuring reliable interactions and efficient backend processes while monitoring performance and iterating on systems based on user feedback.

Design new information architectures for language models, refine their interaction with data, and develop evaluation sets to improve functionality.