Top Remote Senior Level Machine Learning Engineer Jobs in CO

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Design and deploy ML solutions for clinical data, collaborating with cross-functional teams to translate healthcare challenges into scalable systems. Maintain reusable codebases and provide guidance to junior engineers.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

The Senior Machine Learning Engineer will design, implement, and optimize machine learning models, drive AI initiatives, and collaborate cross-functionally to enhance SailPoint's AI capabilities.

As a Staff AI Software Engineer, you'll develop and optimize scalable AI applications, manage deployment pipelines, and collaborate with cross-functional teams to drive innovative solutions.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

Lead AI/machine learning innovations and advanced analytics for new vehicles. Develop statistical models for pricing and valuation, guiding experiments and communicating insights.

Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.

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