Top Tech Jobs & Startup Jobs in Denver & Boulder, CO

Leads strategy, planning, and execution of customer EMR integrations for PhareOS. Oversees end-to-end data delivery, integration quality, process standardization, tooling, vendors, and platform enhancements. Builds and scales the data integration team, manages resources and senior client stakeholders, and drives innovation across integration methods and operating models. Requires extensive healthcare data integration leadership, HL7/FHIR and interface engine expertise, SQL proficiency, and strong program management skills.

Drive budgeting, forecasting, and reforecasting activities; maintain financial models, headcount tracking, and planning files; support month-end close and variance resolution; track labor CAPEX, vendor spend, and accruals; prepare executive financial review materials; and provide business-case and ad hoc analysis to leadership. The role requires advanced Excel skills, strong FP&A fundamentals, and coordination across stakeholders and cost centers.

Owns enterprise learning and leadership development programs from planning through facilitation and evaluation. Creates e-learning content using instructional design principles and authoring tools, maintains program materials, analyzes learner data, partners with communications to promote programs, and manages timelines across multiple initiatives.

Designs, facilitates, and evaluates leadership, career, and development programs. Provides coaching using 360-degree assessments, partners with HR and business leaders to identify talent needs, manages vendors and budgets, and measures program impact through business results, retention, talent pipeline health, and behavior change.

Leads healthcare revenue-cycle consulting engagements focused on mid-cycle medical coding. Assesses current-state processes, develops future-state workflows, translates requirements into implementation plans, provides coding guidance, designs training and change-management strategies, and establishes KPI and SLA monitoring. Partners with executives, operational teams, process engineers, and clients to optimize coding workflows using AI and implement sustainable process improvements.

Designs, builds, and evaluates agentic AI systems for healthcare workflows such as documentation, coding, denial management, and revenue cycle automation. Develops long-horizon agent behavior, evaluation frameworks, benchmarks, feedback loops, monitoring, and production safeguards. Partners with research, ML engineering, clinical, product, and engineering teams to prototype and deploy reliable, explainable, auditable AI systems using LLMs, machine learning, and agentic technologies.

Build and deploy scalable AI/ML systems for retrieval, ranking, categorization, triage, and generative AI across healthcare data. Own solutions end-to-end from problem framing and model selection through production deployment and optimization. Collaborate with software engineers, product managers, clinicians, and operations teams to translate complex business problems into explainable, maintainable systems with measurable outcomes.

Build and deliver AI-powered healthcare products using React, Node.js, Python, and modern full-stack technologies. Partner with customers, designers, researchers, and product teams to understand workflow needs, design intuitive AI experiences, and own features from ideation through deployment, optimization, and scaling. The role requires strong product instincts, end-to-end software engineering ownership, and experience building production-grade applications.

Build and scale AI-native healthcare products through full-stack applications, backend services, data pipelines, and AI systems. Design, test, deploy, and operate production-ready software, run A/B tests, automate manual workflows, and collaborate with product, operations, and AI/ML teams. The role requires end-to-end ownership of scalable, secure, reliable systems and features, with a focus on performance, data integrity, clean code, and measurable business outcomes.

Conduct applied AI research focused on explainability, reinforcement learning, retrieval-augmented generation, and long-context retrieval. Design, train, and evaluate machine learning models using large-scale healthcare data and distributed infrastructure. Build research prototypes and experimentation frameworks, validate measurable impact, and collaborate with ML Engineering, MLOps, and Product teams to transition innovations into production.