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

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.

Own end-to-end extraction and normalization pipelines that convert messy supplier emails and documents into a reliable, queryable data layer. Build entity-resolution, ML/NLP pipelines, agent workflows, evaluation harnesses, and infrastructure for data quality, lineage, monitoring, and model-driven extraction improvements.

As an AI/ML Engineer, you'll design, build, and ship large scale NLP and LLM systems for classification, ranking, and content generation while collaborating with cross-functional teams.

The role involves designing and implementing conversational AI systems, managing prompt orchestration, and ensuring conversational quality through advanced evaluation frameworks.

Own applied AI end-to-end: select and operate foundation models, build Dossbot for natural-language ops and automation, create AI-assisted schema/workflow tooling, design permission-aware agentic automations, and implement evals and observability for safe production use.

Lead and execute research on large language models and agentic systems, running large-scale post-training experiments (SFT, RLHF, RLAIF, DPO, GRPO), building evaluation benchmarks and data synthesis pipelines, translating research into model improvements and production deployments, publishing at top venues, and mentoring researchers and engineers.

Build and ship end-to-end AI voice agent features across backend and frontend, own post-deployment analytics, engage users for feedback, and solve novel product and technical problems.

Lead QA for enterprise conversational AI deployments: design workflow-specific test cases, build AI-driven automated tests, validate prompts, perform daily LLM root cause analysis, debug live calls, monitor ASR/S2S pipelines, and collaborate with engineering and client stakeholders to ensure stable production deployments.

Build and own conversational voice AI products end-to-end: architect, implement, and deploy the platform. Design infrastructure, develop front-end and back-end systems, deliver reliable customer outcomes, and collaborate directly with the CTO to scale the product for enterprise customers.