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.

Implement and maintain record-matching and text-analytics components, translate linguistic theory into production features, optimize search internals (Elasticsearch), collaborate in agile sprints, and help design scalable APIs and architectures to improve record-matching quality and reliability.

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.

Develop intent-recognition models from gaze, gesture, and voice data; implement socially aware navigation algorithms respecting personal space; and design visual and auditory feedback for robot user interfaces. Conduct user studies and rapid prototype AI-driven voice and visual interaction systems to test and refine HRI paradigms.

Participate in a one-time, remote guided English voice recording session (~20 minutes) using your own compatible device. Must follow recording instructions, complete session in a quiet indoor environment, be at least 18, reside and record from an approved U.S. state, and submit one participation only. Approved participants who meet quality standards receive $25.

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.

Lead and grow a team of Conversation Designers to build and scale conversational AI experiences. Partner with Product, Engineering, Analytics, and ML teams to drive strategy, tooling, evaluation, and quality practices. Recruit and remove roadblocks, own strategic initiatives, and remain hands-on in designing, testing, and improving AI-driven conversational products.

Build and own conversational voice AI products end-to-end: architect, implement, and deploy the platform. Design infrastructure, develop front-end and back-end systems, deliver reliable customer outcomes, and collaborate directly with the CTO to scale the product for enterprise customers.

Design, develop, and deploy ML, NLP, and generative AI solutions to improve scientific discovery. Build retrieval, recommendation, ranking, and RAG systems; fine-tune and evaluate LLMs; create evaluation frameworks; build scalable data pipelines; collaborate with cross-functional teams and produce production-quality Python code to deliver reliable, impactful AI systems for research use.

Design, develop, and deploy ML, NLP, and generative-AI solutions (including LLMs and RAG) to improve search, retrieval, recommendation, and question-answering across scientific publications and datasets. Build scalable pipelines, evaluation frameworks, and production-quality Python code while collaborating with engineering, product, UX, and domain experts to measure and improve model reliability, relevance, and user impact.