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

Lead product vision, strategy, and execution for Rovo Chat, Atlassian's AI-driven conversational interface. Prioritize adoption and quality, drive multi-turn conversation improvements, integrate knowledge across first- and third-party sources, partner cross-functionally to ship features, define success metrics, and champion responsible AI practices for safe, transparent conversational experiences.

Lead end-to-end product for voice protection and conversational AI features serving consumers and telecom operator partners. Define product vision, prioritize roadmap, work closely with engineering and research on model training and prototyping, engage customers to validate needs, support commercial and legal partners, and drive product economics and successful launches.

Lead end-to-end delivery of document extraction and NLP solutions for payments: design scalable model pipelines, develop extraction/classification models, define evaluation and lifecycle practices, ensure compliance and explainability, and partner with cross-functional stakeholders to productionize and monitor ML systems.

Design and implement scalable, secure inference services for speech processing. Optimize networking, audio transcoding, latency, memory, and distributed compute orchestration. Debug complex system issues, customize backend services for customers, and partner with Product to deliver end-to-end features.

Lead end-to-end performance of customer-facing conversational AI and knowledge base. Design, test, and optimize AI conversations, define intents and success metrics, analyze KPIs, maintain enterprise knowledge, run continuous improvement using analytics and experimentation, and partner cross-functionally to improve CX and business outcomes.

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, evaluate, and deploy LLM-powered features end-to-end: prompt engineering, semantic retrieval, embeddings, vector stores, evaluation frameworks, and production integration while collaborating with product and engineering and working directly with users.

Lead creation of purpose-built large language models for Cisco's networking, security, data center, and observability domains. Drive end-to-end pre-training, distillation, and continual pre-training efforts, curate datasets and tokenization strategies, train models on large distributed GPU clusters, publish top-tier research, and collaborate with cross-functional teams to deploy research into scalable product solutions.

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.