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

Design and deliver LLM-powered, agentic AI systems to automate customer support and compliance. Build scalable, secure backend infrastructure in Python and Go, integrate third-party models/APIs, implement grounding and guardrails, and measure performance. Lead end-to-end projects, mentor engineers, conduct code reviews, and participate in on-call and incident response to improve resolution times and reduce operational costs.

Design and implement Vercel's agentic interface and agent architecture to understand intent, plan workflows, select tools, recover from failures, and execute long-running tasks. Ship polished conversational and interactive experiences across Slack, web, and other surfaces. Build evaluation, observability, and feedback systems and primitives enabling teams to contribute consistent agent capabilities across the company.

Lead architecture and implementation of AI-native systems that convert natural language into multi-step workflows. Build and scale an LLM agent for real-time inference, dynamic prompting, and tool orchestration. Drive full-stack technical direction, ensure scalability/reliability, integrate learning from usage, mentor engineers, and collaborate with product and design to deliver production-quality, maintainable systems.

Lead architecture and technical vision for AI-native systems that convert natural language into multi-step workflows. Design and scale LLM agents, real-time inference, memory and tool orchestration, and learning systems that evolve from usage data. Set engineering standards, mentor senior engineers, collaborate with product and design, and own ambiguous, end-to-end technical problems to ensure performance, reliability, and extensibility at scale.

Design, build, and operationalize production-grade NLP and GenAI models (summarization, semantic search, classification, prediction). Develop scalable data pipelines and distributed systems, deploy models with cloud-native tooling, mentor engineers, collaborate cross-functionally, and ensure model monitoring, evaluation, and responsible-AI practices to deliver business-aligned insights.

Act as a technical partner to internal teams to design and implement LLM-powered applications. Build prototypes and proofs-of-concept, advise on system-of-system architecture, support end-to-end deployment, and keep teams current on LLM techniques, safety, and best practices while coordinating cross-functional stakeholders.

The Research Engineer will develop large language models, conduct research on model architecture, optimize training infrastructure, and lead research projects.

Lead the Brex Assistant product end-to-end: set strategy and roadmap, define agent capabilities that take actions, design prompts and evals, prototype and iterate using user feedback and traces, and partner with engineering, design, and research to deliver a trustworthy AI agent for finance workflows.

The Research Engineer will design and implement scalable machine learning systems for next-gen language models, optimizing training and inference processes, while working collaboratively and contributing to open-source projects.

Design, deploy, and validate production LLM-based voice agents for customers, integrating with CRMs and telephony. Tune ASR/TTS and conversational turn-taking, build evaluation frameworks, capture product gaps, and transition successful pilots to Professional Services with documentation and playbooks.

Lead research-to-product work on LLM and embedding-driven AdTech features: generate product ideas, prototype with LLMs and embeddings, advance context/persona/embedding models, and take validated concepts end-to-end into production while partnering across research, engineering, product, and marketing.