Top Machine Learning Jobs in Denver & Boulder, CO

Build, evaluate, and deploy LLM/ML models and synthetic datasets to improve Community Support. Identify opportunities via data exploration, analyze structured and unstructured data, automate LLM evaluation, personalize experiences, and collaborate cross-functionally to drive product and operational impact.

Design and evaluate causal inference experiments and causal ML models to measure product impact, optimize resource allocation, and inform support operations. Build, deploy, and iterate statistical/ML models, analyze tradeoffs, collaborate cross-functionally, and communicate insights to influence decisions and scale data science capabilities.

The Staff Machine Learning Engineer will lead ML systems development for personalization and ranking, design architectures, and mentor team members, ensuring reliable operations at scale.

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

The Principal AI/ML Engineer will develop AI/ML solutions for hardware and firmware validation, enhance test efficiency, and collaborate across teams to implement data-driven analytics and automation.

Lead enterprise AI architecture for telecom by designing target-state frameworks, building and deploying production ML models for network optimization and predictive maintenance, integrating AI into OSS/BSS, implementing real-time streaming and edge/cloud solutions, establishing model governance and lifecycle management, and mentoring cross-functional teams to align technology with business outcomes.

The Staff Data Scientist will lead the development and deployment of ML and GenAI systems to detect billing errors and fraud in healthcare payments, collaborating with cross-functional teams.

Own and scale delivery of complex AI data programs (LLM/ML products), ensuring scope, quality, timeline, and margin targets. Translate ambiguous problems into technical workstreams, establish governance, drive solution reviews, embed responsible AI practices, manage vendors, and lead cross-functional teams while partnering with BD and executive stakeholders.

Build and maintain ML and generative AI tooling, training and inference platform services, model evaluation and testing frameworks, and APIs to expose ML products. Deploy and manage applications across the AI/ML SDLC, mentor teammates, and write production-quality code daily.

Provide technical advisory and solution feasibility analysis for enterprise AI initiatives. Evaluate AI engineering approaches, integration complexity, reusable components, and architectural tradeoffs. Support prototyping guidance, vendor capability evaluation, and surface governance, risk, and technical constraints to inform enterprise decisions.

Lead architecture, design, development, deployment, and optimization of an agentic AI platform. Partner with product, data, security, and business teams to productionize AI solutions, establish engineering best practices, mentor teams, and drive reusable AI services, automation, and operational improvements to deliver business impact.

Design, build, and deploy novel sensor hardware and data acquisition systems; curate geoscientific datasets; develop statistical, physics-based, and ML models (2D/3D) to predict mineralization; create visualizations and evaluation tools; collaborate with geoscientists, engineers, and vendors; deploy sensors in the field and integrate results to guide exploration programs.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

Lead perception vision, strategy, and roadmap for AV perception. Drive advances in detection, classification, tracking, fusion, and prediction; design sensor suites; define KPIs; hire and manage engineering teams; coordinate cross-functional deployment and issue resolution to ensure robust commercial autonomy performance.

The Data Scientist II will design, build, and deploy machine learning models and data products, translating business problems into data-driven solutions, while collaborating with various stakeholders and ensuring reliable, production-ready outcomes.

Design, build, and maintain scalable AI/ML pipelines for data processing, model training, and deployment. Collaborate with data scientists and engineers to implement and optimize AI solutions, troubleshoot pipeline issues, document best practices, and develop MVP code to transition into production-ready systems.

Lead Billtrust's Applied Intelligence product strategy, build and scale AI product teams, define governance and evaluation standards, translate frontier AI into production-grade AR solutions, and represent the company externally to drive commercial impact and customer co-creation.

Act as the technical lead in enterprise sales: run technical discovery, design and deliver customized demos and POCs, assess API/cloud/model fit, address technical objections, support contract negotiations, and relay product feedback to engineering while staying current on AI/ML trends.

Lead a centralized Data Science & Engineering team to build scalable data pipelines, productionize ML/AI solutions, ensure data governance and reliability, partner with cross-functional stakeholders to prioritize initiatives, and drive platform improvements for analytics, personalization, and product features.

Lead design and implementation of scalable measurement systems and advanced analytics frameworks that integrate consumption and purchase data. Develop, validate, and deploy statistical, econometric, and machine learning models. Partner with cross-functional and external teams to operationalize AI-enabled research, drive adoption of technology solutions, and build capabilities for consistent, actionable consumer and shopper insights across a global organization.

Lead strategy and build 0->1 AI products that streamline complex healthcare workflows across providers, pharmacies, patients, and pharma partners. Own roadmap, requirements, metrics, and product performance; partner with engineering, data, clinical, and commercial teams to launch and iterate AI-enabled solutions that drive adoption and measurable business and patient outcomes.

Build and ship computer-vision models end-to-end: dataset curation, training, deployment, and monitoring across cloud and roadside edge hardware. Optimize for high-volume inference, accuracy, efficiency, and real-world robustness while collaborating cross-functionally and iterating rapidly.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Design and build reinforcement-learning training environments and diverse tasks to evaluate and improve LLM agents; iterate rapidly on task designs from customer feedback, deliver high-quality outputs with minimal supervision, and maintain PST overlap for collaboration.