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

Evaluate and rewrite AI-generated Ukrainian text for accuracy, grammar, and cultural relevance. Create prompts and natural Ukrainian responses, perform structured ranking and grammatical checks, and collaborate with global teams to improve conversational datasets. Freelance, remote, ~10 hours/week for 1-2 months.

Design, implement, and evaluate advanced NLP/NLU systems for clinical text using transformer and research-oriented neural architectures. Prepare and enrich clinical datasets, run experiments and error analyses, mitigate model risks (bias, drift, hallucination), and partner with ML engineers to deploy models into a compliant cloud environment.

The Applied AI Scientist III develops advanced analytics solutions utilizing AI technologies, leading projects in predictive analytics and NLP to enhance healthcare operations and patient care.

As a Principal Scientist, you will guide the machine learning team in developing natural language applications, focusing on automated scoring and feedback systems. You'll contribute to R&D efforts, support innovation in EdTech, mentor team members, and pursue applied research.

The Solutions Architect will design and implement advanced Conversational AI solutions, manage projects, provide technical insights, and enhance customer value through collaboration and training.

The role involves developing and improving TTS/Voice Generation models, training, fine-tuning, evaluating speech models, and collaborating across research, engineering, and product teams in a fast-paced startup environment.

Develop and deploy advanced ML solutions for OCR and NLP, optimizing models for performance and integrating them into production workflows while collaborating with cross-functional teams.

Develop AI agents leveraging large language models, integrate external tools, and ensure production-ready AI features while collaborating across teams.

The role involves developing and optimizing machine learning systems for voice experiences, focusing on speech recognition and language understanding. Responsibilities include training models, ensuring performance metrics, and collaborating with cross-functional teams for successful deployment.

Design, fine-tune, evaluate, and deploy LLM-driven, user-facing browser features using RAG, embeddings, prompt engineering, and modern ML tooling. Lead end-to-end model lifecycle, run production experiments, collaborate with product and engineering, and document and review work with an emphasis on privacy, latency, and usability.