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

Design and implement scalable, secure inference services for speech processing. Optimize networking, audio transcoding, latency, memory, and distributed compute orchestration. Debug complex system issues, customize backend services for customers, and partner with Product to deliver end-to-end features.

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

Lead design and implementation of LLM-powered conversational financial interfaces. Architect secure, zero-trust orchestration (tools, chains, policy layers), own full-stack services and data pipelines, partner with product, and mentor global teams to deliver production-grade intelligent customer experiences.

Design and implement conversational and agentic AI solutions, build natural language interfaces and voice experiences, prototype UIs, conduct user research and usability testing, integrate AI platforms into contact center systems, and collaborate with cross-functional teams to optimize user interaction engineering and client outcomes.

Convert ambiguous healthcare and operational problems into well-scoped ML/AI, ranking, optimization, NLP, or LLM tasks; build and evaluate models, run error analysis, design honest evaluations, partner with engineering and clinical stakeholders, and productionize reliable models with clear communication of tradeoffs and limitations.

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.

Design and implement Vercel's agentic interface and agent architecture to understand intent, plan workflows, select tools, recover from failures, and execute long-running tasks. Ship polished conversational and interactive experiences across Slack, web, and other surfaces. Build evaluation, observability, and feedback systems and primitives enabling teams to contribute consistent agent capabilities across the company.

Own end-to-end voice platform migration and scaling: LiveKit migration, real-time turn-taking, VAD, SIP/WebRTC, multi-agent handoff, training/fine-tuning voice models, inference cost optimization, and retrieval-based grounding using Supabase/pgvector.

Lead end-to-end performance of customer-facing conversational AI and knowledge base. Design, test, and optimize AI conversations, define intents and success metrics, analyze KPIs, maintain enterprise knowledge, run continuous improvement using analytics and experimentation, and partner cross-functionally to improve CX and business outcomes.

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.