Top Remote Senior Level Machine Learning Engineer Jobs in CO

Lead global commercial and operational initiatives to accelerate Private AI adoption. Establish governance and operating rhythms, run monthly business reviews, prioritize accounts and use cases, define strategic metrics, align ecosystem and commercial strategies, and build business cases and growth models to guide executive decision-making.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

Build, deploy, and maintain AI-powered agents, APIs, and applications for People operations on Snowflake. Turn messy business requirements into production systems, integrate with Workday/Notion/case tools, ensure data governance and security, design multi-model LLM reliability controls, and operate full lifecycle including CI/CD, monitoring, and incident fixes while partnering with non-technical stakeholders.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.

Lead advanced analytics and ML efforts to extract insights from large-scale historical data, design features and data representations for models and LLM/agentic systems, partner with product and engineering to build data pipelines and prototypes, evaluate AI workflows, and drive innovation through experiments and empirical analysis to inform product strategy.

Build and operate Coinbase's LLM and agent platform: unified model gateway, AI Hub, secure microVM agent runtime, knowledge base integrations, AI FinOps, observability and evaluation tooling, and production-grade agent deployments with teams across the company.

Leads and manages an applied machine learning team developing fraud detection and identity verification models. Responsibilities include people management, coaching, code review, production ML systems, model lifecycle ownership, experimentation, deployment, monitoring, fraud research, cross-functional prioritization, and communicating results. The role remains hands-on while partnering with Product, Engineering, Risk, and leadership to drive measurable business impact and shape future product strategy using machine learning and AI.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

Lead complex modeling projects combining deep learning, LLMs, and traditional techniques. Prototype, deploy, and monitor production models, mentor data scientists, collaborate with engineering/product, and translate results into business impact for insurance customers.

Lead and deliver productionized ML solutions across audience, marketing, pharma direct, and employer analytics. Design segmentation, propensity, attribution, and identity models; apply causal inference and experimentation; operationalize models with MLOps; evaluate generative AI/LLM use cases; set technical roadmap and mentor data scientists to drive measurable business impact.