Top Natural Language Processing (NLP) Jobs in Seattle, WA

The role involves developing AI systems for processing voice data, optimizing ML workflows, and collaborating on ML and LLM solutions.

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.

Collaborate with Ai2 researchers on language-model and NLP research, gain hands-on experience with Ai2's data and engineering infrastructure, co-author papers for top venues, and prepare for PhD studies. Work is research-focused with mentorship and no teaching/admin duties. On-site work in Seattle is expected.

Develop intent-recognition models from gaze, gesture, and voice data; implement socially aware navigation that respects personal space; and design visual and auditory feedback for robot user interfaces. Run user studies and rapidly prototype AI-driven voice and visual interfaces.

Join Welo Data’s remote Welsh AI contributor network for flexible projects involving text, image, or audio annotation; search, ad, and chatbot evaluation; prompt creation; and linguistic or cultural quality review. Projects vary from several days to weeks, with scope, timelines, and compensation provided before each assignment. This is a talent community application, not an active job opening.

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.

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.

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.

Build and optimize conversational AI and agentic virtual agents on Genesys Cloud. Responsibilities include prompt engineering, NLU model development, intent and entity design, production conversation analysis, evaluation framework creation, performance optimization, scripting, and customer collaboration. The role focuses on improving reliability, task completion, edge-case handling, and user experience across LLM and NLU systems.