Top Machine Learning Jobs in Denver & Boulder, CO

Lead development and integration of predictive pricing models for home insurance, modernize state rating plans, support rate filings, monitor performance and recommend pricing actions, analyze competitor strategies, develop pricing for expansion, build internal pricing analytics software, and coach/mentor analysts.

Partner with a sports-sector client to design, build, and deploy end-to-end machine learning solutions. Own modeling lifecycle, feature engineering, validation, automation, and integration with engineering teams. Use SQL and Python to extract data, build models (including NLP for surveys), and deliver reproducible analytics and measurable business impact.

Lead design and deployment of reinforcement learning and sequential decision models for collections and recovery. Build scalable ML pipelines (Databricks/Spark), run experimentation and offline policy evaluation, collaborate with engineering/MLOps to productionize models, and mentor junior data scientists.

Lead development of time-series and econometric models (ARIMA, VAR, state-space, WLS, panel data) and Bayesian probabilistic frameworks. Use causal inference and stochastic processes to produce actionable insights, work with large datasets on Databricks, communicate results to stakeholders, and mentor junior team members.

Design and implement conversational AI solutions using NLP, ML, and LLMs. Build ETL pipelines and integrate relational and NoSQL databases. Develop Voice AI interactions on Vertex AI, work with Nuance IVR and Microsoft platforms, and write production-grade Python code.

Design, build, and deploy conversational AI solutions on the Kore.AI platform. Create and configure chatbots (intents, entities, dialog flows), integrate with back-end systems and REST APIs, and develop responsive user interfaces using ReactJS alongside UI/UX designers.

Design, build, and deploy conversational agents using Microsoft Copilot Studio. Gather stakeholder requirements, implement NLU/NLP, integrate with enterprise systems, test and refine agents, and provide documentation and user support for HR, policy retrieval, and procure-to-pay automations.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.

Lead the machine learning strategy, design scalable ML systems, drive MLOps, mentor engineers, and comply with AI governance.

Independently validate and monitor machine learning models for credit underwriting, credit risk, and fraud detection. Identify model weaknesses, recommend improvements, and collaborate with model owners to remediate findings. Support and maintain the Model Risk Management framework and coordinate with Internal Audit, Internal Controls, and Compliance on audit and regulatory requests.

Lead development and roadmap of AI-powered products for internal teams and advertisers, translating AI concepts into product requirements, partnering with engineering, data science, sales, and ad ops to launch targeting, creative, analytics, and automation solutions.

Research, deploy, and maintain AI models and automation solutions; integrate systems and APIs; manage AWS infrastructure and PostgreSQL databases; build BI dashboards; monitor and optimize AI systems and provide technical support.

The AI Executive Partner will advise C-Suite executives on leveraging AI to drive business outcomes, manage member relationships, and deliver innovative AI solutions.

Lead and deliver complex, cross-team AI product programs, drive agent-first SDLC adoption, ensure integration and operational readiness of AI workflows, define metrics, communicate risks and status, and standardize TPM best practices across Product and Engineering.

Contribute data science and statistical expertise to design analytical challenges, create high-quality training data, evaluate AI-generated solutions, build evaluation frameworks, and collaborate with researchers to improve LLM quantitative reasoning and model performance.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and 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.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

Lead the strategy and execution of AI and data products, focusing on sales and marketing initiatives while partnering with business teams to define and deliver impactful solutions across operational systems.

The Principal AI/ML Research Engineer leads research in architecture and algorithms for AI/ML, mentors junior researchers, and collaborates with teams to innovate and implement solutions.

Join Trexquant as a Global Alpha Researcher to develop trading signals, implement research, and apply machine learning for alpha discovery.

The Sr. Director, IT is accountable for leading Data & AI special projects, aligning strategies, ensuring resource efficiency, and overseeing project health and governance.

Lead a multidisciplinary team to design, deploy, and optimize ML-driven bidding systems and measurement for a nine-figure marketing budget. Drive experimentation, partner strategy, media-mix optimization, and end-to-end measurement while aligning Marketing, Analytics, Engineering, and Product to improve marketing ROI and scale performance.

Lead portfolio-level Data & AI strategy and technical governance for enterprise clients. Advise C-suite, drive architecture and modern data stacks, win high-value engagements, and scale engineering and data science teams.

Lead product strategy and roadmap for enterprise data platforms, BI, analytics, and AI/ML products. Align investments with business priorities, partner with engineering and data teams, ensure governance and responsible AI, measure adoption and business impact, and build and mentor cross-functional product teams to drive platform adoption and value.