Top Remote Senior Level Machine Learning Engineer Jobs in CO

Develop and productionize a next-gen Dispatch System, build ML models, automate processes, and collaborate with various teams while mentoring other engineers.

The Senior Applied Scientist will build models for credit risk and fraud functions, leveraging machine learning and collaborating with teams to derive insights from data.

Design and deploy machine learning algorithms, develop production systems, and collaborate with stakeholders to align ML solutions with business objectives.

The Senior Machine Learning Engineer will evaluate and apply advanced ML models, integrate with company infrastructure, and support data pipeline orchestration while focusing on autonomous vehicle technology.

The Staff Data Scientist will build and manage machine learning models to optimize personalization, inventory, and logistics while leading the ML function and collaborating cross-functionally.

Lead development of machine learning models for real-time content ranking, translating business objectives into effective ML solutions while managing experiments and data pipelines.

The Machine Learning Scientist will develop models and systems for content metadata and discovery at Netflix, collaborating with teams to operationalize AI solutions. The role requires significant experience and collaboration with stakeholders.

As a Machine Learning Engineer, you will develop and operationalize models for content understanding and discovery, collaborating with cross-functional teams and stakeholders to solve impactful problems using machine learning.

Design and implement ML-driven solutions for user engagement and content discovery. Collaborate across teams to enhance product features using advanced machine learning models.

The Sr. Staff Machine Learning Engineer will lead architecture and development of ML systems, enhance patient interactions, optimize treatment recommendations, and mentor engineers.

The Senior Machine Learning Engineer will design and maintain ML solutions for fraud detection, collaborating across teams and establishing MLOps best practices.