Top Artificial Intelligence Machine Learning Engineer Jobs in CO

The Director of Engineering will lead the AI Inference team, optimizing LLM inference pipelines and driving research and development in inference technologies while mentoring developers.

The Machine Learning Engineer will design, develop, and deploy ML models using Google Cloud tools, focusing on Document AI and Vertex AI, ensuring scalable applications and efficient performance.

The role involves designing and deploying ML models to extract knowledge from enterprise data, fine-tuning LLMs/SLMs, and collaborating on a scalable AI platform.

The Machine Learning Engineer will design and build systems that improve the AI shopping agent by using real user feedback, focusing on feedback loops and metrics to enhance user interactions and agent performance.

Sift Healthcare seeks a Senior AI Engineer to enhance ML and AI infrastructure, improve predictive models for healthcare payments, and collaborate with cross-functional teams, requiring 5+ years in an AI role and advanced degree.

The Forward Deployed ML Engineer will integrate AI/ML models into clinical software, assist in model fine-tuning, and engage with clients to ensure success in healthcare deployments.

Design, build, and deploy end-to-end production ML systems for audience targeting, lookalike generation, and propensity scoring. Own ML lifecycle from experimentation to production monitoring, leverage distributed/cloud platforms, implement vector similarity and embedding systems, engineer scalable features, and collaborate with engineering and product teams to ensure model quality and operational reliability.

The ML Engineering Intern will contribute to the Nebula LLM development, tackle machine learning challenges, and gain hands-on experience in AI and language technology.

Lead the design, execution, and analysis of machine learning-driven product experiments to optimize ordering systems. Collaborate with stakeholders and mentor teams.

As a Machine Learning Engineer, you'll evaluate neural network quality, coordinate with researchers, manage experiments, and report results to streamline AI software development.