Top Natural Language Processing Jobs in Denver & Boulder, CO

Coordinate recruiting operations across technical and business hiring, including interview scheduling, candidate communications, travel logistics, feedback collection, and ATS data hygiene. The role aims to improve scheduling speed and accuracy through AI-native workflows, automation, templates, and process improvements while delivering a clear, responsive candidate experience.

Own end-to-end localization operations for enterprise clients, serving as the primary operational contact and trusted advisor. Coordinate stakeholders and cross-functional teams, manage localization workflows and configurations across TMS and project tools, resolve client and internal issues, and improve processes through documentation, automation, and AI-enabled solutions. The role requires localization or translation project management experience, client communication skills, familiarity with localization formats and CAT/TMS tools, and strong organizational abilities.

Work through a backlog of unconverted marketing leads using phone, AI-assisted LinkedIn prospecting, and persistent outreach. Qualify prospect interest, arrange well-prepared handoffs to Account Executives, maintain accurate CRM records, and share prospect feedback with Sales and Marketing. The role primarily targets financial advisors and wealth management professionals and focuses on increasing marketing-lead conversion.

Develops and commercializes partner-built solutions across the Superhuman Suite. Responsibilities include shaping joint value propositions, creating pitch and demo assets, enabling alliance managers and partners, packaging repeatable solutions, coordinating launches, supporting partner growth, and tracking post-launch performance. The role works across Alliance Programs, Partner Marketing, Technical Alliances, Product, and solution architecture, with approximately 25% international travel.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

Join Welo Data’s Hungarian contributor network for flexible, remote AI data projects. Depending on project needs, contributors may annotate text, images, or audio; evaluate search results, advertisements, or chatbot responses; create and test prompts for large language models; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with scope, timelines, and compensation provided before participation.

Lead the vision, roadmap, development, and delivery of cybersecurity threat intelligence products and the Global Intel Graph portfolio. Manage large-scale intelligence data, standardize cybersecurity entities, define integrations, and partner with engineering and domain experts. Validate SME data and AI-generated intelligence through labeling, accuracy checks, edge-case testing, and customer feedback. Lead cross-functional teams, communicate strategy to executives, and build scalable capabilities that improve threat detection and response.

Own the end-to-end new business sales process for Fortune 1000 corporate accounts. Responsibilities include researching and prospecting opportunities, building relationships, conducting product demonstrations, managing trials, closing deals, forecasting accurately, developing pipeline, partnering with SDRs and product specialists, supporting customer health and upsell opportunities, and sharing client feedback with sales and product teams.

Develop and maintain enterprise and SaaS backend applications using Java and Spring Boot. Design RESTful APIs, manage authentication and authorization, build database models and queries, conduct code reviews, improve application performance and security, and collaborate with technical and business stakeholders. The role also involves testing, deployment, documentation, and maintaining cloud-based applications using AWS and Docker.

Senior backend engineer responsible for designing and implementing new features, scaling and optimizing the platform, improving Postgres and Elasticsearch performance, fine-tuning LLMs, and making architectural decisions. Collaborate with engineering team, take ownership of features, write performant, maintainable code, and help shape product and infrastructure for data-intensive applications (NLP, search, indexing).