Top Natural Language Processing (NLP) Jobs in Los Angeles, CA

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.

Lead end-to-end design and execution of voice data programs: build ingestion pipelines, labeling specs, tooling, and quality safeguards; align data strategy with research and product; manage vendors and teams; track throughput, quality, and vendor performance; and prototype/deploy data tools to scale datasets that improve speech and voice AI model outcomes.

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, build, deploy, and operate end-to-end human-centered agentic AI systems across frontend, backend, data, and intelligence components. Integrate AI/ML/NLP into workflows, implement agent behaviors, support sales with demos and POCs, create scalable APIs/services, and provide technical guidance for proposals and delivery.

Lead and grow a data science team to design, prototype, and productionize NLP/LLM systems for legal content. Set AI strategy, build evaluation and monitoring, drive RAG/agentic solutions, collaborate cross-functionally, and ensure scalable, reliable deployment and responsible use of generative AI.

Design, build, test, and deploy agentic, multi-step LLM-driven AI workflows for clinical operations and research. Integrate multimodal data, implement RAG/grounding, ensure reliability, traceability, and security, and collaborate with clinicians and engineers to deliver production-ready systems.

Design, develop, and deploy machine learning and NLP solutions at scale. Work with cross-functional teams to build content classification, representation learning, search, and recommendation models using classical ML, deep learning, and LLMs. Process large datasets with Python, SQL, and Spark, and communicate results to stakeholders with clear documentation.