Principal Machine Learning Engineer - Robotics

Oracle Corporation; United States; Presencial; USD 120.000 - 180.000; Jornada completa

Descripción de la vacante

Oracle Corporation is seeking an ML Engineer to implement and productionize ML models, ensuring readiness for deployment and automating workflows. You will build infrastructure to monitor model performance in production and collaborate with stakeholders to integrate ML solutions into existing systems.

The role requires developing, maintaining, and refining internal tools, writing clean, bug-free code, and staying up-to-date with advances in ML to continuously improve modeling practices and data

Responsabilidades

• Implements machine learning (ML) models for production.
• Ensures the readiness of machine learning models for deployment in production.
• Automates machine learning workflows.
• Creates infrastructure and frameworks to monitor the performance of machine learning models in deployment.
• Evaluates potential data quality, security, and/or privacy issues and their impacts on modeling.
• Provides troubleshooting and debugging support.
• Addresses issues in machine learning infrastructure and workflows.
• Collaborates with stakeholders to integrate machine learning models into new or extant systems.
• Develops, maintains, and refines tools, platforms, and services for internal use.
• Develops efficient, bug-free code from scratch.
• Maintains familiarity with current developments in the machine learning field and integrates knowledge into model development.

Descripción del empleo

• Implements machine learning (ML) models for production.
• Ensures the readiness of machine learning models for deployment in production.
• Automates machine learning workflows.
• Creates infrastructure and frameworks to monitor the performance of machine learning models in deployment.
• Evaluates potential data quality, security, and/or privacy issues and their impacts on modeling.
• Provides troubleshooting and debugging support.
• Addresses issues in machine learning infrastructure and workflows.
• Collaborates with stakeholders to integrate machine learning models into new or extant systems.
• Develops, maintains, and refines tools, platforms, and services for internal use.
• Develops efficient, bug-free code from scratch.
• Maintains familiarity with current developments in the machine learning field and integrates knowledge into model development.