Top Natural Language Processing (NLP) Jobs in Denver & Boulder, CO

Lead development and implementation of AI-driven solutions focused on text classification and NLP. Build and optimize ML models, preprocess and transform text data, combine supervised/active/semi-supervised approaches, create tagged datasets, and provide technical leadership while collaborating cross-functionally to align AI capabilities with product and business goals.

Develop and iterate post-training methods for large language models: drive data collection, build evaluations, design experiments, and apply reward modeling and RL techniques to improve model capabilities and deployment.

Define and own platform architecture for agentic and conversational AI systems, lead cross-functional technical initiatives, design scalable LLM integration and retrieval pipelines, establish engineering standards for scale, mentor senior engineers, and operate full-time on-site in San Francisco.

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.

Evaluate AI-generated responses (English, French, or both) using detailed annotation guidelines. Perform side-by-side comparisons, assess factuality, relevance, reasoning, tone, and cultural appropriateness for French (France). Document decisions, meet productivity and quality standards, complete training and qualification, and participate in calibration and feedback cycles.

The Principal LLM Application Engineer will develop and optimize Large Language Models, lead projects, evaluate performance, and collaborate across teams to enhance AI capabilities in cybersecurity.

Build and own production AI agents for assigned customers: discover workflows, design prompts and conversation flows, integrate with customer APIs/CRMs, create test sets, measure and harden agents, monitor live conversations, debug and ship fixes, and feed product improvements. Work customer-embedded to deliver reliable, secure, scalable LLM-powered voice and conversational AI solutions.

Build full-stack features for enterprise-scale voice and chat AI: integrate ASR/TTS and conversational LLMs, optimize production systems for latency and cost, participate in on-call rotations, and collaborate with product and ML teams to ship high-impact, scalable solutions.

Produce original, human-authored Yoruba texts across specified domains from provided prompts to support language-data and AI evaluation. Follow confidentiality rules, attest originality, meet deadlines, and work independently with domain-appropriate style and tone.