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

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.

Build, productionize, and operate AI-driven features and integrations across frontend, backend, and model layers. Prototype and deliver agent-driven experiences (conversational interfaces, RAG, tool-calling), integrate LLMs and external data sources, and collaborate with product and design. Participate in on-call/standby duties.

Design, build, evaluate, and scale production-ready AI solutions (LLMs, NLP, retrieval, and generative AI) to support scientific discovery. Develop RAG systems, semantic search, summarization, QA, ranking, recommendation, and evaluation frameworks. Integrate knowledge graphs and domain metadata, partner with engineering and product teams, lead technical decisions, and mentor data scientists.

Record natural, conversational customer-service style speech from a home studio. Submit two audition samples; if selected, complete two supervised ~4-hour remote recording sessions meeting technical audio specs.

Reviews written content and AI-generated responses for accuracy, clarity, grammar, coherence, completeness, and guideline adherence. Provides concise, constructive feedback, identifies subtle linguistic errors, documents findings using evaluation tools, and consistently applies evaluation criteria during a four-week remote contract sprint requiring at least 20 hours per week.

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.

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.

Leads automated scoring and natural language application research for educational assessment products. Responsibilities include setting team priorities, mentoring scientists, designing and analyzing studies, developing machine learning applications, collaborating with stakeholders, presenting technical findings, writing reports, publishing applied research, and supporting products involving automated scoring, crisis-content detection, and formative feedback.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

Lead research and engineering work on LLM-based architectures for dialog, search, and destination entry. Optimize LLMs via prompting and fine-tuning, integrate them with ASR, TTS, and vision systems, and collaborate cross-functionally to deliver performant, resource-constrained solutions for automotive voice assistants.