Top Natural Language Processing (NLP) Jobs in Boston, MA

The Senior Machine Learning Engineer will develop, deploy, and optimize large language models on healthcare data, collaborate with teams, and ensure compliance.

Develop and optimize medical large language models, collaborate with interdisciplinary teams, and maintain state-of-the-art machine learning techniques to enhance medical education and clinical decision support.

The role involves developing and optimizing large language models for robotaxis, enhancing their understanding of urban environments, and integrating models into decision-making processes.

The Senior Data Scientist in Conversational AI & NLU designs and optimizes AI solutions, evaluates models, collaborates with stakeholders, and analyzes conversational data to improve user experience.

The role involves researching and developing training strategies for large language models, focusing on their applications in scientific reasoning and performance evaluation.

The Principal Engineer for Conversational AI will lead engineering teams, develop high-quality AI software solutions, and ensure operational excellence. They will oversee design and code reviews, mentor team members, and drive technical vision in cloud environments.

The role involves fine-tuning large language models (LLMs) for specific tasks, optimizing performance, and evaluating models on GPU infrastructure. Ideal for advanced students or those with relevant practical experience.

As an AI Intern, you will build a natural-language interface for cluster operations, with supervision from senior team members. You'll focus on generating an MCP server from OpenAPI specs and integrating it with an LLM reasoning agent for cluster management tasks.

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.