Top Natural Language Processing (NLP) Jobs in Chicago, IL

Design and implement conversational and agentic AI solutions, build natural language interfaces and voice experiences, prototype UIs, conduct user research and usability testing, integrate AI platforms into contact center systems, and collaborate with cross-functional teams to optimize user interaction engineering and client outcomes.

Develop and operationalize ML and deep learning solutions (especially NLP/LLMs) for Wholesale Lending. Design experiments, build batch and real-time prediction pipelines and end-to-end AutoML/AutoNLP workflows, collaborate with product, data, engineering, legal and compliance teams, and deploy models to production on AWS.

Build, evaluate, and deploy NLP and LLM-based systems (text classification, information extraction, retrieval/RAG). Create evaluation frameworks, golden test sets, and annotation guidelines. Analyze performance, surface failure modes, partner on production deployment/monitoring, and document best practices while collaborating with cross-functional teams.

Design, train, and implement domain-specific LLMs for automation in restoration companies. Collaborate with teams, coordinate AI engineering, and ensure model reliability.

Own and evolve revenue calibration models tying CX interactions to revenue, design causal inference and experimentation frameworks, build LLM-powered classification/NLP pipelines, productionize models with Analytics Engineers, define segmentation and behavioral signals, and ensure statistical rigor for executive reporting and regulatory defensibility.

Embed with Compliance to identify automation opportunities, rapidly prototype LLM-based solutions on Coinbase's AI platform, own the Compliance automation roadmap, ensure auditability and human-in-the-loop controls, define build-vs-buy strategy, and drive adoption across Compliance teams.

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.

Own end-to-end voice platform migration and scaling: LiveKit migration, real-time turn-taking, VAD, SIP/WebRTC, multi-agent handoff, training/fine-tuning voice models, inference cost optimization, and retrieval-based grounding using Supabase/pgvector.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

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.

Co-founder & CTO will own product and engineering: harden voice/LLM systems for production, build the RaaS delivery infrastructure, optimize unit economics, represent technology to customers and investors, convert pilots to paying customers, expand AI staff offerings, and recruit and lead the founding engineering team.