Top Natural Language Processing Jobs in Denver & Boulder, CO

Conduct talent mapping, market research, proactive sourcing, and pipeline development across technical, business, and leadership roles. Partner with recruiters and hiring managers to define target profiles, identify passive candidates, analyze talent availability and compensation trends, and deliver actionable market intelligence. Build evergreen talent communities, improve research workflows and tools, and adapt quickly to changing hiring priorities.

Own full-cycle recruiting across all company functions and experience levels. Responsibilities include posting jobs, screening applicants, building shortlists, coordinating interviews and debriefs, maintaining ATS data, partnering with hiring managers, improving recruiting processes, and delivering a strong candidate experience across multiple simultaneous searches.

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.

Develop product launch narratives and messaging that connect with customers and prospects. Create demos, case studies, webinars, campaigns, web content, and customer stories. Partner cross-functionally with product, sales, and customer success to ensure consistent positioning. Conduct customer and prospect research, identify market trends, and use data-driven insights to strengthen product marketing strategy.

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.

Lead an established RevOps and Enablement team to sharpen analytics, harden systems and data architecture, and scale AI-first workflows across Sales, Marketing, and CX. Own revenue tech stack, forecasting, GTM reporting, process optimization, and enablement programs while partnering with executive leadership to drive measurable revenue outcomes.

Review, localize, fact-check, translate, and adapt primarily football-related content from English into Japanese. Conduct market research on terminology and localization trends, follow style guidelines, provide editorial feedback, and ensure content resonates with Japanese audiences. The role requires native-level Japanese, excellent English, strong sports knowledge, and experience in translation, localization, editing, or sports journalism.

Join Welo Data’s global contributor network for flexible, remote AI projects. Depending on skills, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and requirements provided before each assignment.

Design, develop, and evolve Algolia’s Salesforce B2C Commerce integration, owning its architecture, scalability, reliability, and long-term direction. Lead technical work from design through delivery, write RFCs, resolve complex integration issues, improve testing and maintainability, and contribute to roadmap planning. Collaborate across Engineering, Product, Support, and customer-facing teams while developing scalable integrations with external APIs and commerce platforms.

Owns revenue enablement programs for North American field teams from design through rollout, measurement, and iteration. Partners with Product, Marketing, Sales Leadership, and RevOps to align go-to-market priorities. Develops playbooks, certifications, training, and competitive resources; operationalizes MEDDPICC; tracks enablement KPIs; improves content platforms and AI workflows; coaches sellers; and translates field insights into updated enablement materials.

Develop and maintain prompts, responses, and datasets for large language models. Lead labeling initiatives with internal teams and third-party firms, create stakeholder guidelines, and train teams on LLM and dataset development best practices. The role requires native-level English, bilingual proficiency in an additional language, specialized domain knowledge, and a college degree or equivalent experience.