Top Natural Language Processing Jobs in Denver & Boulder, CO

Leads measurement and product marketing for eight enterprise AI platforms. Builds shared KPI, taxonomy, scorecard, data-quality, and value-realization frameworks; translates usage and outcome data into executive insights and investment guidance. Oversees positioning, internal launches, campaigns, enablement, adoption, and audience segmentation. Partners across product, engineering, data, communications, and business teams while building and managing teams responsible for analytics, marketing, and communications.

Leads product strategy, roadmap, lifecycle ownership, adoption, and measurable outcomes for a greenfield enterprise AI platform. Defines platform boundaries, governance controls, evaluation, registration, and cost-tracking capabilities while translating evolving risk, privacy, security, and GxP requirements into usable product features. Partners with engineering, design, legal, compliance, risk, security, and global agent-building teams to prioritize investments, guide delivery, communicate direction, and drive platform adoption.

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.

Manage full-cycle talent acquisition, including candidate screening, assessment calls, communication, sourcing, pipeline development, recruiting platform management, and applicant tracking data. Partner with hiring managers to meet hiring goals while delivering an excellent candidate experience and strengthening the employer brand. The role requires proactive outreach, strong organization, relationship-building, adaptability, and effective remote collaboration.

Leads Deepgram’s end-to-end Text-to-Speech research program, owning research strategy, technical direction, model development, evaluation, and production deployment. Advances neural audio modeling, prosody, expressiveness, multilingual generation, voice consistency, controllability, and inference performance. Builds and develops research teams, manages technical leaders, prioritizes experiments and compute, evaluates model quality, and partners with engineering and product leadership to ship production-grade models.

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.

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.

Own success for enterprise customers by delivering security-focused enablement, projects, advisory services, training, workflow development, and strategic success plans. Manage escalations, support issues, business reviews, customer value communication, and product feedback while integrating threat intelligence into security operations. Maintain account documentation in Zendesk, Gainsight, and JIRA, support at-risk accounts, engage technical and executive stakeholders, and travel at least 30%.

Create challenging, expert-level questions about animals and pets in Brazilian Portuguese, write ideal responses, and decompose them into cited facts with sources and relevance. This remote freelance engagement supports AI training-data development, requires PhD-level expertise, and involves approximately 60 flexible hours over two months. Contributors use consumer-grade LLMs for research support and prompt-difficulty calibration while producing original content.

Creates challenging, expert-level questions about Brazilian law and ethics, writes ideal answers in Brazilian Portuguese, and breaks responses into cited facts with sources and relevance. The role supports AI training data development through original expert content, with AI tools used only for research assistance. This is a remote, flexible freelance engagement totaling approximately 60 hours over two months.

Design, develop, and maintain features for Gradio and Trackio, architect Python frameworks, collaborate with contributors, and enhance user experience for ML web apps.

Supports deal structuring, pricing, discounting, contract terms, approvals, and redline resolution throughout the sales cycle. Partners with Sales, Legal, Finance, and RevOps to apply pricing guardrails, resolve exceptions, improve deal review processes, and document repeatable workflows. The role also helps embed AI into CPQ, CLM, and CRM workflows while maintaining metrics such as cycle time, approval turnaround, discount rates, SLA attainment, and deal quality.

Leads AlphaSense’s Financial Services channel research coverage across banking, insurance, and fintech. Develops coverage roadmaps, channel-check frameworks, expert source strategies, and structured questionnaires. Guides analysts and operations teams on project execution, expert targeting, feasibility, and research quality. Acts as the primary financial services domain expert, identifying companies, subsectors, value chains, and research opportunities while improving workflows and ensuring insights are consistent, actionable, and aligned with investor demand.