Top Natural Language Processing Jobs in Denver & Boulder, CO

Leads SMB and commercial sales strategy, revenue performance, team development, pipeline forecasting, enablement, hiring, and complex enterprise deals. Partners with Product, Marketing, Customer Success, and executive leadership to scale go-to-market execution, improve sales consistency, and drive customer expansion across a multi-product SaaS environment.

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.

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.

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.

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.

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).

Own end-to-end client relationships for a book of B2B SaaS customers: drive adoption, engagement, retention, and expansion of AI-powered contract drafting tools. Lead onboarding, training, large-scale rollouts, and translate customer feedback into product improvements while designing scalable CS processes and tracking health metrics to demonstrate value.

Own Ada’s partner co-selling strategy, working with account executives and strategic partners to source, develop, and close partner-influenced deals. Build joint go-to-market plans, account and territory strategies, marketplace revenue through AWS and Microsoft, and partner-sourced pipeline. Maintain accurate Salesforce records, streamline deal registration and contracting, enable internal and external sellers, and identify ecosystem opportunities for future investment.

Develops and maintains prompts, responses, guidelines, and datasets for generative AI and large language model tools. Leads labeling initiatives with internal teams and third-party firms, trains stakeholders on LLM and dataset best practices, and supports quality assurance and testing. Requires native-level English, bilingual ability in a specified language, specialized domain knowledge, and a college degree or equivalent experience.

Designs and develops enterprise and multitenant SaaS applications using Java, Spring Boot, and AWS. Responsibilities include cloud architecture, REST APIs, databases, infrastructure as code, CI/CD, authentication, tenant provisioning, monitoring, security compliance, and performance improvements. The role collaborates with technical and business stakeholders and supports FedRAMP requirements.

Audit linguist submissions containing audio, orthographic transcriptions, and AI-generated IPA transcriptions. Verify pronunciation accuracy against recordings, identify review errors or inconsistencies, provide corrections when required, and meet project quality and turnaround standards using a client-owned platform.