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

Lead the content layer for AI-driven customer support tools, authoring dialogue flows and maintaining AI content for optimal customer experience. Analyze performance and improve workflows, ensuring accurate and safe interactions across channels.

The role involves training and deploying ASR models, improving pronunciation feedback, measuring performance, and expanding ASR systems for language learning.

Partner-facing engineer who builds, debugs, and deepens ChatGPT integrations with third-party messaging platforms. Responsibilities include prototyping, resolving API/auth/latency issues, translating privacy and data-handling requirements, creating integration guides and playbooks, coordinating cross-functional launches, and periodically traveling to meet international partners.

You will design and build LLM-powered features, integrate AI capabilities into workflows, and contribute to internal practices at a healthcare-focused company.

Design and optimize enterprise-grade conversational AI across voice, chat, SMS, and email. Advise customers, evaluate voice quality, build playbooks and evaluation frameworks, enable customer teams, and partner with Engineering and Product to drive quality improvements.

Build and improve NLP, retrieval, and LLM systems to extract structured knowledge from messy enterprise data. Work on prompting, fine-tuning, RAG, retrieval stacks (semantic search, vector storage, hybrid approaches), evaluation harnesses, and production pipelines. Collaborate with customers and engineers to define correctness, catch regressions, and ship robust, maintainable ML solutions.

Design, implement, deploy, and maintain LLM-based AI systems for workflow automation, process and entity extraction, dialog/agent functionality, and text generation. Work includes research, model evaluation, production deployment, monitoring, statistical analysis, and collaboration with engineering and product teams using cloud infrastructure (AWS/GCP).

The Applied AI Engineer will work with language models to create applications, manage the model alpha program with OpenAI, conduct experiments, and influence policies.

As a Machine Learning Engineer, you will develop models for NLP and code-generation systems, analyze data, and improve AI performance using advanced algorithms. You'll manage the entire ML lifecycle and ensure effective communication of model benefits across the organization.

Design, build, and operate LLM-facing backend services and evaluation systems. Develop prompts, context pipelines, tool-calling workflows, and retrieval systems. Measure and improve model grounding, consistency, latency, and cost. Ship production-ready solutions and rigorous regression/evaluation pipelines for real-world therapeutic data.