Top Natural Language Processing (NLP) Jobs in Austin, TX

As an AI Software Intern, you'll design and prototype workflows using large language models, work with structured data, evaluate AI model outputs, and participate in agile development processes.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

As a Junior Research Enablement Engineer, you will develop tools for the LanguageAI research team, focusing on machine learning tasks. Responsibilities include designing applications, working with data services, conducting research, and enhancing product quality.

Lead and mentor teams in developing language model-based AI solutions, ensuring high technical standards, quality implementations, and effective communication with stakeholders.

Develop and maintain applications using MCP and ChatGPT frameworks, optimize AI interactions, and prototype innovative solutions. Collaborate on product architecture with minimal supervision.

As a Senior Customer Experience Consultant, you will design and implement engaging conversational flows for Intelligent Virtual Assistants, ensuring exceptional customer interactions through effective dialogue scripting and collaboration with AI engineers.

The Senior Applied Scientist will research and engineer NLP solutions, design algorithms, train LLMs, and contribute to platform development.

The role involves developing language models for voice systems, analyzing linguistic data, and maintaining existing voice applications at Roku. It requires expertise in linguistics and NLP technologies.

As a Lead Data Engineer, you'll design and implement AI-powered data systems, focusing on natural language queries and scalable frameworks. Collaborate with teams to optimize data access and enhance reporting tools.

The Senior AI Platform Engineer will design, build, and deploy conversational AI systems, manage technical delivery for clients, and ensure reliability and performance in production, handling real-time constraints and error recovery during implementations.