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

Work across AI, backend, and frontend: curate and clean writing datasets, fine-tune large language models, run training experiments, develop metrics and eval sets, build backend services in Python/Django, and implement frontend features with React.

The Principal LLM Application Engineer will develop and optimize Large Language Models, lead projects, evaluate performance, and collaborate across teams to enhance AI capabilities in cybersecurity.

Develop and deploy NLP pipelines: fetch and preprocess unstructured text/audio, build deep learning models (TensorFlow/PyTorch) for classification, regression, clustering, anomaly detection and structured outputs, work with embeddings, entity resolution, and collaborate with the data science team on product development.

As a Forward Deployed AI Engineer, you will develop and implement voice AI solutions for enterprise clients, ensuring system reliability and customer satisfaction while managing technical discussions and deployments.

Review and annotate Korean text and images (bounding boxes, structured questions, short summaries) following annotation guidelines to train AI systems. Work remote, 30-40 hrs/week for at least 3 months; on-the-job training provided.

Lead Conversational AI Developer responsible for developing chatbots and virtual assistants, integrating AI technologies, and improving user interactions through data analysis and software development.

As an AI Conversation Designer, you'll create and optimize chatbot flows, analyze performance, and collaborate cross-functionally to enhance customer interactions using AI and NLP technologies.

Train and fine-tune large language models, build and maintain the full LLM stack and agentic pipelines, improve chat/agent algorithms, translate NLP research into roadmap, and collaborate with validation, content, and dataset teams to design experiments and measure model quality.

Lead architecture and productionization of NLP, voice, and ML systems; mentor engineers; integrate AI features with backend services on AWS; build evaluation, monitoring, and responsible-AI safeguards; drive AI strategy and technical vision.

Design and implement NLP and LLM-powered pipelines to extract, summarize, and semantically search technical documentation. Fine-tune transformer models, build RAG systems, convert unstructured documents into structured knowledge, optimize inference for production, and collaborate with engineers and SMEs to deploy document intelligence solutions.