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

Lead research and engineering work on LLM-based architectures for dialog, search, and destination entry. Optimize LLMs via prompting and fine-tuning, integrate them with ASR, TTS, and vision systems, and collaborate cross-functionally to deliver performant, resource-constrained solutions for automotive voice assistants.

Founding clinical AI researcher bridging medicine and engineering: define clinical requirements, curate and annotate datasets, develop and validate AI/NLP/LLM models for hospital quality and patient safety, engage hospital partners, design prompts and workflows, and produce clinical documentation and validation reports for product and regulatory use.

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.

As an ML research engineer, you will develop systems using language models, curate datasets, set evaluation metrics, and scale semantic search processes.

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

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

Build and prototype state-of-the-art large language model systems and deep AI assistants, take services from ideation to production, fine-tune and compare open-source and commercial LLMs, use cloud platforms, analyze model and projection accuracy, and collaborate cross-functionally.

Build and own end-to-end production modules for an AI-native fintech platform using LLMs. Ship weekly releases, create evaluation harnesses to verify model outputs, and maintain quality in live banking environments. Work on-agentic and conversational AI systems in a fast-paced, on-site San Francisco team.